
!pip install yellowbrick

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# this will help in making the Python code more structured automatically (good coding practice)
# %load_ext nb_black

# Libraries to help with reading and manipulating data
import numpy as np
import pandas as pd

# Libraries to help with data visualization
import matplotlib.pyplot as plt
plt.rcParams.update({'figure.max_open_warning': 0})

%matplotlib inline

import seaborn as sns

# Removes the limit for the number of displayed columns
pd.set_option("display.max_columns", None)
# Sets the limit for the number of displayed rows
pd.set_option("display.max_rows", 200)

# to scale the data using z-score
from sklearn.preprocessing import StandardScaler
from scipy.stats import zscore

# to compute distances
from scipy.spatial.distance import cdist, pdist

# to perform k-means clustering and compute silhouette scores
from sklearn.model_selection  import train_test_split
from sklearn.cluster import KMeans
from sklearn.metrics import silhouette_score

# to perform hierarchical clustering, compute cophenetic correlation, and create dendrograms
from sklearn.cluster import AgglomerativeClustering
from scipy.cluster.hierarchy import dendrogram, linkage, cophenet
from scipy.spatial.distance import pdist

# to visualize the elbow curve and silhouette scores
from yellowbrick.cluster import KElbowVisualizer, SilhouetteVisualizer

# to perform PCA
from sklearn.decomposition import PCA

import warnings
warnings.filterwarnings("ignore", category=DeprecationWarning)


# loading the dataset
# EFIndexData_Orig is a dataframe created from csv file
# index_col is setting the dataframe's index to "Country" Column

EFIndexData_Orig = pd.read_csv('economic_freedom_index_2019_data.csv',index_col = 'Country',encoding='ISO-8859-1')


EFIndexData = EFIndexData_Orig.copy()


EFIndexData.shape

(186, 33)


# viewing a random sample of the dataset
EFIndexData.sample(n=50, random_state=1)


EFIndexData.info()

<class 'pandas.core.frame.DataFrame'>
Index: 186 entries, Afghanistan to Zimbabwe
Data columns (total 33 columns):
 #   Column                       Non-Null Count  Dtype  
---  ------                       --------------  -----  
 0   CountryID                    186 non-null    int64  
 1   Country Name                 186 non-null    object 
 2   WEBNAME                      186 non-null    object 
 3   Region                       186 non-null    object 
 4   World Rank                   180 non-null    float64
 5   Region Rank                  180 non-null    float64
 6   2019 Score                   180 non-null    float64
 7   Property Rights              185 non-null    float64
 8   Judical Effectiveness        185 non-null    float64
 9   Government Integrity         185 non-null    float64
 10  Tax Burden                   180 non-null    float64
 11  Gov't Spending               183 non-null    float64
 12  Fiscal Health                183 non-null    float64
 13  Business Freedom             185 non-null    float64
 14  Labor Freedom                184 non-null    float64
 15  Monetary Freedom             184 non-null    float64
 16  Trade Freedom                182 non-null    float64
 17  Investment Freedom           184 non-null    float64
 18  Financial Freedom            181 non-null    float64
 19  Tariff Rate (%)              182 non-null    float64
 20  Income Tax Rate (%)          183 non-null    float64
 21  Corporate Tax Rate (%)       183 non-null    float64
 22  Tax Burden % of GDP          179 non-null    float64
 23  Gov't Expenditure % of GDP   182 non-null    float64
 24  Population (Millions)        186 non-null    object 
 25  GDP (Billions, PPP)          185 non-null    object 
 26  GDP Growth Rate (%)          184 non-null    float64
 27  5 Year GDP Growth Rate (%)   183 non-null    float64
 28  GDP per Capita (PPP)         184 non-null    object 
 29  Unemployment (%)             181 non-null    object 
 30  Inflation (%)                182 non-null    float64
 31  FDI Inflow (Millions)        181 non-null    object 
 32  Public Debt (% of GDP)       182 non-null    float64
dtypes: float64(24), int64(1), object(8)
memory usage: 49.4+ KB


# Replace the abbrevated column names containing the word "Gov't" with "Government".

EFIndexData.columns=[c.replace("Gov't", "Government") for c in EFIndexData.columns]


# Replace "(%)" in the column names with the string "Percent".

EFIndexData.columns=[c.replace("(%)", "Percent") for c in EFIndexData.columns]


# Replace "%" in the column names with the string "Percent".

EFIndexData.columns=[c.replace("%", "Percent") for c in EFIndexData.columns]


# Replace "(Millions)" in the column names with the string "in Millions".

EFIndexData.columns=[c.replace("(Millions)", "in Millions") for c in EFIndexData.columns]


# Replace "(Billions, PPP)" in the column names with the string "in Billions by PPP in USA Dollars".

EFIndexData.columns=[c.replace("(Billions, PPP)", "in Billions by PPP in USA Dollars") for c in EFIndexData.columns]


# Replace "(Percent of GDP)" in the column names with the string "by Percent of GDP".

EFIndexData.columns=[c.replace("(Percent of GDP)", "by Percent of GDP") for c in EFIndexData.columns]


# Replace "(PPP)" in the column names with the string "by PPP".

EFIndexData.columns=[c.replace("(PPP)", "by PPP") for c in EFIndexData.columns]


EFIndexData.columns=[c.replace("GDP per Capita by PPP", "GDP per Capita by PPP in USA Dollars") for c in EFIndexData.columns]


# Replace the spaces in the column names with "_".

EFIndexData.columns=[c.replace(" ", "_") for c in EFIndexData.columns]


EFIndexData.info()

<class 'pandas.core.frame.DataFrame'>
Index: 186 entries, Afghanistan to Zimbabwe
Data columns (total 33 columns):
 #   Column                                  Non-Null Count  Dtype  
---  ------                                  --------------  -----  
 0   CountryID                               186 non-null    int64  
 1   Country_Name                            186 non-null    object 
 2   WEBNAME                                 186 non-null    object 
 3   Region                                  186 non-null    object 
 4   World_Rank                              180 non-null    float64
 5   Region_Rank                             180 non-null    float64
 6   2019_Score                              180 non-null    float64
 7   Property_Rights                         185 non-null    float64
 8   Judical_Effectiveness                   185 non-null    float64
 9   Government_Integrity                    185 non-null    float64
 10  Tax_Burden                              180 non-null    float64
 11  Government_Spending                     183 non-null    float64
 12  Fiscal_Health                           183 non-null    float64
 13  Business_Freedom                        185 non-null    float64
 14  Labor_Freedom                           184 non-null    float64
 15  Monetary_Freedom                        184 non-null    float64
 16  Trade_Freedom                           182 non-null    float64
 17  Investment_Freedom_                     184 non-null    float64
 18  Financial_Freedom                       181 non-null    float64
 19  Tariff_Rate_Percent                     182 non-null    float64
 20  Income_Tax_Rate_Percent                 183 non-null    float64
 21  Corporate_Tax_Rate_Percent              183 non-null    float64
 22  Tax_Burden_Percent_of_GDP               179 non-null    float64
 23  Government_Expenditure_Percent_of_GDP_  182 non-null    float64
 24  Population_in_Millions                  186 non-null    object 
 25  GDP_in_Billions_by_PPP_in_USA_Dollars   185 non-null    object 
 26  GDP_Growth_Rate_Percent                 184 non-null    float64
 27  5_Year_GDP_Growth_Rate_Percent          183 non-null    float64
 28  GDP_per_Capita_by_PPP_in_USA_Dollars    184 non-null    object 
 29  Unemployment_Percent                    181 non-null    object 
 30  Inflation_Percent                       182 non-null    float64
 31  FDI_Inflow_in_Millions                  181 non-null    object 
 32  Public_Debt_by_Percent_of_GDP           182 non-null    float64
dtypes: float64(24), int64(1), object(8)
memory usage: 49.4+ KB


# Remove the underscore character "_" at the end of the column names.

# EFIndexData['Investment_Freedom_'] = EFIndexData['Investment_Freedom']
# EFIndexData['Government_Expenditure_Percent_of_GDP_'] = EFIndexData['Government_Expenditure_Percent_of_GDP']

EFIndexData.columns=[c.replace("Investment_Freedom_", "Investment_Freedom") for c in EFIndexData.columns]
EFIndexData.columns=[c.replace("Government_Expenditure_Percent_of_GDP_", "Government_Expenditure_Percent_of_GDP") for c in EFIndexData.columns]


EFIndexData.info()

<class 'pandas.core.frame.DataFrame'>
Index: 186 entries, Afghanistan to Zimbabwe
Data columns (total 33 columns):
 #   Column                                 Non-Null Count  Dtype  
---  ------                                 --------------  -----  
 0   CountryID                              186 non-null    int64  
 1   Country_Name                           186 non-null    object 
 2   WEBNAME                                186 non-null    object 
 3   Region                                 186 non-null    object 
 4   World_Rank                             180 non-null    float64
 5   Region_Rank                            180 non-null    float64
 6   2019_Score                             180 non-null    float64
 7   Property_Rights                        185 non-null    float64
 8   Judical_Effectiveness                  185 non-null    float64
 9   Government_Integrity                   185 non-null    float64
 10  Tax_Burden                             180 non-null    float64
 11  Government_Spending                    183 non-null    float64
 12  Fiscal_Health                          183 non-null    float64
 13  Business_Freedom                       185 non-null    float64
 14  Labor_Freedom                          184 non-null    float64
 15  Monetary_Freedom                       184 non-null    float64
 16  Trade_Freedom                          182 non-null    float64
 17  Investment_Freedom                     184 non-null    float64
 18  Financial_Freedom                      181 non-null    float64
 19  Tariff_Rate_Percent                    182 non-null    float64
 20  Income_Tax_Rate_Percent                183 non-null    float64
 21  Corporate_Tax_Rate_Percent             183 non-null    float64
 22  Tax_Burden_Percent_of_GDP              179 non-null    float64
 23  Government_Expenditure_Percent_of_GDP  182 non-null    float64
 24  Population_in_Millions                 186 non-null    object 
 25  GDP_in_Billions_by_PPP_in_USA_Dollars  185 non-null    object 
 26  GDP_Growth_Rate_Percent                184 non-null    float64
 27  5_Year_GDP_Growth_Rate_Percent         183 non-null    float64
 28  GDP_per_Capita_by_PPP_in_USA_Dollars   184 non-null    object 
 29  Unemployment_Percent                   181 non-null    object 
 30  Inflation_Percent                      182 non-null    float64
 31  FDI_Inflow_in_Millions                 181 non-null    object 
 32  Public_Debt_by_Percent_of_GDP          182 non-null    float64
dtypes: float64(24), int64(1), object(8)
memory usage: 49.4+ KB


# EFIndexData = EFIndexData.drop(columns = ['Investment_Freedom_', 'Government_Expenditure_Percent_of_GDP_'])


# we will drop the CountryID column add it adds no value to the analysis

EFIndexData.drop("CountryID", axis=1, inplace=True)


# The Region attribute is a categorical variable data type.

EFIndexData.Region = EFIndexData.Region.astype('category')


EFIndexData.info()

<class 'pandas.core.frame.DataFrame'>
Index: 186 entries, Afghanistan to Zimbabwe
Data columns (total 32 columns):
 #   Column                                 Non-Null Count  Dtype   
---  ------                                 --------------  -----   
 0   Country_Name                           186 non-null    object  
 1   WEBNAME                                186 non-null    object  
 2   Region                                 186 non-null    category
 3   World_Rank                             180 non-null    float64 
 4   Region_Rank                            180 non-null    float64 
 5   2019_Score                             180 non-null    float64 
 6   Property_Rights                        185 non-null    float64 
 7   Judical_Effectiveness                  185 non-null    float64 
 8   Government_Integrity                   185 non-null    float64 
 9   Tax_Burden                             180 non-null    float64 
 10  Government_Spending                    183 non-null    float64 
 11  Fiscal_Health                          183 non-null    float64 
 12  Business_Freedom                       185 non-null    float64 
 13  Labor_Freedom                          184 non-null    float64 
 14  Monetary_Freedom                       184 non-null    float64 
 15  Trade_Freedom                          182 non-null    float64 
 16  Investment_Freedom                     184 non-null    float64 
 17  Financial_Freedom                      181 non-null    float64 
 18  Tariff_Rate_Percent                    182 non-null    float64 
 19  Income_Tax_Rate_Percent                183 non-null    float64 
 20  Corporate_Tax_Rate_Percent             183 non-null    float64 
 21  Tax_Burden_Percent_of_GDP              179 non-null    float64 
 22  Government_Expenditure_Percent_of_GDP  182 non-null    float64 
 23  Population_in_Millions                 186 non-null    object  
 24  GDP_in_Billions_by_PPP_in_USA_Dollars  185 non-null    object  
 25  GDP_Growth_Rate_Percent                184 non-null    float64 
 26  5_Year_GDP_Growth_Rate_Percent         183 non-null    float64 
 27  GDP_per_Capita_by_PPP_in_USA_Dollars   184 non-null    object  
 28  Unemployment_Percent                   181 non-null    object  
 29  Inflation_Percent                      182 non-null    float64 
 30  FDI_Inflow_in_Millions                 181 non-null    object  
 31  Public_Debt_by_Percent_of_GDP          182 non-null    float64 
dtypes: category(1), float64(24), object(7)
memory usage: 46.9+ KB


# Check missing values
def missing_values(df):
    miss_val = df.isnull().sum()
    miss_val_percent = df.isnull().sum() / len(df) * 100
    cols = EFIndexData.columns.values
    sort_ = np.argsort(cols)
    indices = pd.Series(sort_[np.searchsorted(cols,cols,sorter=sort_)],
                       index=cols)
    miss_val_table = pd.concat([indices, miss_val, miss_val_percent],
                               axis=1)
    miss_val_df = miss_val_table.rename(
    columns = {0: "Column Index", 1: "Missing Values", 2: "% Missing"})
    return miss_val_df


missing_values(EFIndexData)


na_count = EFIndexData.isnull().sum(axis=1)
na_EFIndexData = na_count[na_count > 0]
pd.concat([EFIndexData.loc[na_EFIndexData.index]['Country_Name'],na_EFIndexData], 
          axis=1).rename(columns={0: "NA Count"})


# checking for duplicate values
EFIndexData.duplicated().sum()

0


l_df = len(EFIndexData)
# Remove missing data
EFIndexData = EFIndexData.dropna()


# Checking missing values
EFIndexData.isna().sum()

Country_Name                             0
WEBNAME                                  0
Region                                   0
World_Rank                               0
Region_Rank                              0
2019_Score                               0
Property_Rights                          0
Judical_Effectiveness                    0
Government_Integrity                     0
Tax_Burden                               0
Government_Spending                      0
Fiscal_Health                            0
Business_Freedom                         0
Labor_Freedom                            0
Monetary_Freedom                         0
Trade_Freedom                            0
Investment_Freedom                       0
Financial_Freedom                        0
Tariff_Rate_Percent                      0
Income_Tax_Rate_Percent                  0
Corporate_Tax_Rate_Percent               0
Tax_Burden_Percent_of_GDP                0
Government_Expenditure_Percent_of_GDP    0
Population_in_Millions                   0
GDP_in_Billions_by_PPP_in_USA_Dollars    0
GDP_Growth_Rate_Percent                  0
5_Year_GDP_Growth_Rate_Percent           0
GDP_per_Capita_by_PPP_in_USA_Dollars     0
Unemployment_Percent                     0
Inflation_Percent                        0
FDI_Inflow_in_Millions                   0
Public_Debt_by_Percent_of_GDP            0
dtype: int64


print("%s Percent of Original Data Remaining." 
    %round(len(EFIndexData) / l_df * 100, 2))

93.01 Percent of Original Data Remaining.


print("%s Countries Remaining in the Data Set." 
    %len(EFIndexData))

173 Countries Remaining in the Data Set.


EFIndexData.describe()


EFIndexData.info()

<class 'pandas.core.frame.DataFrame'>
Index: 173 entries, Afghanistan to Zimbabwe
Data columns (total 32 columns):
 #   Column                                 Non-Null Count  Dtype   
---  ------                                 --------------  -----   
 0   Country_Name                           173 non-null    object  
 1   WEBNAME                                173 non-null    object  
 2   Region                                 173 non-null    category
 3   World_Rank                             173 non-null    float64 
 4   Region_Rank                            173 non-null    float64 
 5   2019_Score                             173 non-null    float64 
 6   Property_Rights                        173 non-null    float64 
 7   Judical_Effectiveness                  173 non-null    float64 
 8   Government_Integrity                   173 non-null    float64 
 9   Tax_Burden                             173 non-null    float64 
 10  Government_Spending                    173 non-null    float64 
 11  Fiscal_Health                          173 non-null    float64 
 12  Business_Freedom                       173 non-null    float64 
 13  Labor_Freedom                          173 non-null    float64 
 14  Monetary_Freedom                       173 non-null    float64 
 15  Trade_Freedom                          173 non-null    float64 
 16  Investment_Freedom                     173 non-null    float64 
 17  Financial_Freedom                      173 non-null    float64 
 18  Tariff_Rate_Percent                    173 non-null    float64 
 19  Income_Tax_Rate_Percent                173 non-null    float64 
 20  Corporate_Tax_Rate_Percent             173 non-null    float64 
 21  Tax_Burden_Percent_of_GDP              173 non-null    float64 
 22  Government_Expenditure_Percent_of_GDP  173 non-null    float64 
 23  Population_in_Millions                 173 non-null    object  
 24  GDP_in_Billions_by_PPP_in_USA_Dollars  173 non-null    object  
 25  GDP_Growth_Rate_Percent                173 non-null    float64 
 26  5_Year_GDP_Growth_Rate_Percent         173 non-null    float64 
 27  GDP_per_Capita_by_PPP_in_USA_Dollars   173 non-null    object  
 28  Unemployment_Percent                   173 non-null    object  
 29  Inflation_Percent                      173 non-null    float64 
 30  FDI_Inflow_in_Millions                 173 non-null    object  
 31  Public_Debt_by_Percent_of_GDP          173 non-null    float64 
dtypes: category(1), float64(24), object(7)
memory usage: 43.6+ KB


EFIndexData.head(180)


col = "Population_in_Millions"

EFIndexData[col] = EFIndexData[col].str.strip()


EFIndexData['GDP_in_Billions_by_PPP_in_USA_Dollars'] = EFIndexData['GDP_in_Billions_by_PPP_in_USA_Dollars'].replace({'\$':''}, regex = True)
EFIndexData['GDP_in_Billions_by_PPP_in_USA_Dollars'] = EFIndexData['GDP_in_Billions_by_PPP_in_USA_Dollars'].replace({'\,':''}, regex = True)

EFIndexData['GDP_per_Capita_by_PPP_in_USA_Dollars'] = EFIndexData['GDP_per_Capita_by_PPP_in_USA_Dollars'].replace({'\$':''}, regex = True)
EFIndexData['GDP_per_Capita_by_PPP_in_USA_Dollars'] = EFIndexData['GDP_per_Capita_by_PPP_in_USA_Dollars'].replace({'\,':''}, regex = True)

col1 = "GDP_in_Billions_by_PPP_in_USA_Dollars"
EFIndexData[col1] = EFIndexData[col1].str.strip()

col2 = "GDP_per_Capita_by_PPP_in_USA_Dollars"
EFIndexData[col2] = EFIndexData[col2].str.strip()


EFIndexData.head()


col4 = "FDI_Inflow_in_Millions"

EFIndexData[col4] = EFIndexData[col4].str.replace(",", "").str.strip()
EFIndexData.head()


# changing FDI_Inflow_in_Millions column datatype from Python object to a numeric float.

EFIndexData[col4] = EFIndexData[col4].astype(float)


EFIndexData.info()

<class 'pandas.core.frame.DataFrame'>
Index: 173 entries, Afghanistan to Zimbabwe
Data columns (total 32 columns):
 #   Column                                 Non-Null Count  Dtype   
---  ------                                 --------------  -----   
 0   Country_Name                           173 non-null    object  
 1   WEBNAME                                173 non-null    object  
 2   Region                                 173 non-null    category
 3   World_Rank                             173 non-null    float64 
 4   Region_Rank                            173 non-null    float64 
 5   2019_Score                             173 non-null    float64 
 6   Property_Rights                        173 non-null    float64 
 7   Judical_Effectiveness                  173 non-null    float64 
 8   Government_Integrity                   173 non-null    float64 
 9   Tax_Burden                             173 non-null    float64 
 10  Government_Spending                    173 non-null    float64 
 11  Fiscal_Health                          173 non-null    float64 
 12  Business_Freedom                       173 non-null    float64 
 13  Labor_Freedom                          173 non-null    float64 
 14  Monetary_Freedom                       173 non-null    float64 
 15  Trade_Freedom                          173 non-null    float64 
 16  Investment_Freedom                     173 non-null    float64 
 17  Financial_Freedom                      173 non-null    float64 
 18  Tariff_Rate_Percent                    173 non-null    float64 
 19  Income_Tax_Rate_Percent                173 non-null    float64 
 20  Corporate_Tax_Rate_Percent             173 non-null    float64 
 21  Tax_Burden_Percent_of_GDP              173 non-null    float64 
 22  Government_Expenditure_Percent_of_GDP  173 non-null    float64 
 23  Population_in_Millions                 173 non-null    object  
 24  GDP_in_Billions_by_PPP_in_USA_Dollars  173 non-null    object  
 25  GDP_Growth_Rate_Percent                173 non-null    float64 
 26  5_Year_GDP_Growth_Rate_Percent         173 non-null    float64 
 27  GDP_per_Capita_by_PPP_in_USA_Dollars   173 non-null    object  
 28  Unemployment_Percent                   173 non-null    object  
 29  Inflation_Percent                      173 non-null    float64 
 30  FDI_Inflow_in_Millions                 173 non-null    float64 
 31  Public_Debt_by_Percent_of_GDP          173 non-null    float64 
dtypes: category(1), float64(25), object(6)
memory usage: 43.6+ KB


# Changing Population_in_Millions column datatype from Python object to a numeric float.

EFIndexData.Population_in_Millions = EFIndexData.Population_in_Millions.astype(float)


# Changing GDP_per_Capita_by_PPP_in_USA_Dollars column datatype from Python object to a numeric float.

EFIndexData.GDP_per_Capita_by_PPP_in_USA_Dollars = EFIndexData.GDP_per_Capita_by_PPP_in_USA_Dollars.astype(float)


# Changing Unemployment_Percent column datatype from Python object to a numeric float.

EFIndexData.Unemployment_Percent = EFIndexData.Unemployment_Percent.astype(float)


# Changing GDP_in_Billions_by_PPP_in_USA_Dollars column datatype from Python object to a numeric float.

EFIndexData.GDP_in_Billions_by_PPP_in_USA_Dollars = EFIndexData.GDP_in_Billions_by_PPP_in_USA_Dollars.astype(float)


EFIndexData.info()

<class 'pandas.core.frame.DataFrame'>
Index: 173 entries, Afghanistan to Zimbabwe
Data columns (total 32 columns):
 #   Column                                 Non-Null Count  Dtype   
---  ------                                 --------------  -----   
 0   Country_Name                           173 non-null    object  
 1   WEBNAME                                173 non-null    object  
 2   Region                                 173 non-null    category
 3   World_Rank                             173 non-null    float64 
 4   Region_Rank                            173 non-null    float64 
 5   2019_Score                             173 non-null    float64 
 6   Property_Rights                        173 non-null    float64 
 7   Judical_Effectiveness                  173 non-null    float64 
 8   Government_Integrity                   173 non-null    float64 
 9   Tax_Burden                             173 non-null    float64 
 10  Government_Spending                    173 non-null    float64 
 11  Fiscal_Health                          173 non-null    float64 
 12  Business_Freedom                       173 non-null    float64 
 13  Labor_Freedom                          173 non-null    float64 
 14  Monetary_Freedom                       173 non-null    float64 
 15  Trade_Freedom                          173 non-null    float64 
 16  Investment_Freedom                     173 non-null    float64 
 17  Financial_Freedom                      173 non-null    float64 
 18  Tariff_Rate_Percent                    173 non-null    float64 
 19  Income_Tax_Rate_Percent                173 non-null    float64 
 20  Corporate_Tax_Rate_Percent             173 non-null    float64 
 21  Tax_Burden_Percent_of_GDP              173 non-null    float64 
 22  Government_Expenditure_Percent_of_GDP  173 non-null    float64 
 23  Population_in_Millions                 173 non-null    float64 
 24  GDP_in_Billions_by_PPP_in_USA_Dollars  173 non-null    float64 
 25  GDP_Growth_Rate_Percent                173 non-null    float64 
 26  5_Year_GDP_Growth_Rate_Percent         173 non-null    float64 
 27  GDP_per_Capita_by_PPP_in_USA_Dollars   173 non-null    float64 
 28  Unemployment_Percent                   173 non-null    float64 
 29  Inflation_Percent                      173 non-null    float64 
 30  FDI_Inflow_in_Millions                 173 non-null    float64 
 31  Public_Debt_by_Percent_of_GDP          173 non-null    float64 
dtypes: category(1), float64(29), object(2)
memory usage: 43.6+ KB


# viewing a random sample of the dataset

EFIndexData.sample(n=50, random_state=1)


# A statistical summary of the data.

EFIndexData.describe(include="all").T


# function to plot a boxplot and a histogram along the same scale.

def histogram_boxplot(data, feature, figsize=(12, 7), kde=False, bins=None):
    """
    Boxplot and histogram combined

    data: dataframe
    feature: dataframe column
    figsize: size of figure (default (12,7))
    kde: whether to the show density curve (default False)
    bins: number of bins for histogram (default None)
    """
    f2, (ax_box2, ax_hist2) = plt.subplots(
        nrows=2,  # Number of rows of the subplot grid= 2
        sharex=True,  # x-axis will be shared among all subplots
        gridspec_kw={"height_ratios": (0.25, 0.75)},
        figsize=figsize,
    )  # creating the 2 subplots
    sns.boxplot(
        data=data, x=feature, ax=ax_box2, showmeans=True, color="violet"
    )  # boxplot will be created and a star will indicate the mean value of the column
    sns.histplot(
        data=data, x=feature, kde=kde, ax=ax_hist2, bins=bins, palette="winter"
    ) if bins else sns.histplot(
        data=data, x=feature, kde=kde, ax=ax_hist2
    )  # For histogram
    ax_hist2.axvline(
        data[feature].mean(), color="green", linestyle="--"
    )  # Add mean to the histogram
    ax_hist2.axvline(
        data[feature].median(), color="black", linestyle="-"
    )  # Add median to the histogram


# selecting numerical columns

num_col = EFIndexData.select_dtypes(include=np.number).columns.tolist()

for item in num_col:
    histogram_boxplot(EFIndexData, item)


cols_list = EFIndexData.select_dtypes(include=np.number).columns.to_list()
cols_list.remove("World_Rank")
cols_list.remove("Region_Rank")


fig, axes = plt.subplots(9, 3, figsize=(25, 25))
fig.suptitle("CDF plot of numerical variables", fontsize=20)
counter = 0
for ii in range(9):
     sns.ecdfplot(ax=axes[ii][0], x=EFIndexData[cols_list[counter]])
     counter = counter + 1

     sns.ecdfplot(ax=axes[ii][1], x=EFIndexData[cols_list[counter]])
     counter = counter + 1

     sns.ecdfplot(ax=axes[ii][2], x=EFIndexData[cols_list[counter]])
     counter = counter + 1

fig.tight_layout(pad=2.0)


# function to create labeled barplots


def labeled_barplot(data, feature, perc=False, n=None):
    """
    Barplot with percentage at the top

    data: dataframe
    feature: dataframe column
    perc: whether to display percentages instead of count (default is False)
    n: displays the top n category levels (default is None, i.e., display all levels)
    """

    total = len(data[feature])  # length of the column
    count = data[feature].nunique()
    if n is None:
        plt.figure(figsize=(count + 1, 5))
    else:
        plt.figure(figsize=(n + 1, 5))

    plt.xticks(rotation=90, fontsize=15)
    ax = sns.countplot(
        data=data,
        x=feature,
        palette="Paired",
        order=data[feature].value_counts().index[:n].sort_values(),
    )

    for p in ax.patches:
        if perc:
            label = "{:.1f}%".format(
                100 * p.get_height() / total
            )  # percentage of each class of the category
        else:
            label = p.get_height()  # count of each level of the category

        x = p.get_x() + p.get_width() / 2  # width of the plot
        y = p.get_height()  # height of the plot

        ax.annotate(
            label,
            (x, y),
            ha="center",
            va="center",
            size=12,
            xytext=(0, 5),
            textcoords="offset points",
        )  # annotate the percentage

    plt.show()  # show the plot


# Let's explore Region further
labeled_barplot(EFIndexData, "Region", perc=True)


EFIndexData.groupby('Region').size()

Region
Americas                        30
Asia-Pacific                    40
Europe                          43
Middle East and North Africa    14
Sub-Saharan Africa              46
dtype: int64


plt.figure(figsize=(25, 15))
sns.heatmap(EFIndexData[num_col].corr(), annot=True, vmin=-1, vmax=1, fmt=".1f", cmap="Spectral")
plt.show()


sns.pairplot(data=EFIndexData[cols_list], diag_kind="kde")
plt.show()


EFIndexData.groupby('Region')['2019_Score'].mean().sort_values().plot.barh()

<matplotlib.axes._subplots.AxesSubplot at 0x7fca23ade7d0>


EFIndexData.groupby('Region')['2019_Score'].std()

Region
Americas                        10.759597
Asia-Pacific                    10.765804
Europe                           6.927649
Middle East and North Africa     9.324304
Sub-Saharan Africa               7.217708
Name: 2019_Score, dtype: float64


sns.pairplot(data=EFIndexData[num_col], diag_kind="kde")
plt.show()


EFIndexData.drop("Tariff_Rate_Percent", axis=1, inplace=True)


scaler = StandardScaler()
to_exclude = [
    "CountryID",
    "Country_Name",
    "WEBNAME",
    "Region",
    "World_Rank",
    "Region_Rank",
    "Country",
]

# columns to be used for clustering are
col_for_clustering = [c for c in EFIndexData.columns if c not in to_exclude]
print(col_for_clustering)

['2019_Score', 'Property_Rights', 'Judical_Effectiveness', 'Government_Integrity', 'Tax_Burden', 'Government_Spending', 'Fiscal_Health', 'Business_Freedom', 'Labor_Freedom', 'Monetary_Freedom', 'Trade_Freedom', 'Investment_Freedom', 'Financial_Freedom', 'Income_Tax_Rate_Percent', 'Corporate_Tax_Rate_Percent', 'Tax_Burden_Percent_of_GDP', 'Government_Expenditure_Percent_of_GDP', 'Population_in_Millions', 'GDP_in_Billions_by_PPP_in_USA_Dollars', 'GDP_Growth_Rate_Percent', '5_Year_GDP_Growth_Rate_Percent', 'GDP_per_Capita_by_PPP_in_USA_Dollars', 'Unemployment_Percent', 'Inflation_Percent', 'FDI_Inflow_in_Millions', 'Public_Debt_by_Percent_of_GDP']


EFIndexData_scaled = EFIndexData[col_for_clustering].copy()
EFIndexData_scaled.iloc[:, :] = scaler.fit_transform(EFIndexData_scaled.iloc[:, :])
EFIndexData_scaled.head()


# creating dataframe copies for k-means and hierarchical clustering
km_EFIndexData = EFIndexData.copy()
hc_EFIndexData = EFIndexData.copy()


clusters = range(2, 11)
meanDistortions = []

for k in clusters:
    model = KMeans(n_clusters=k)
    model.fit(EFIndexData_scaled)
    prediction = model.predict(EFIndexData_scaled)
    distortion = (
        sum(
            np.min(cdist(EFIndexData_scaled, model.cluster_centers_, "euclidean"), axis=1)
        )
        / EFIndexData_scaled.shape[0]
    )

    meanDistortions.append(distortion)

    print("Number of Clusters:", k, "\tAverage Distortion:", distortion)

plt.plot(clusters, meanDistortions, "bx-")
plt.xlabel("k")
plt.ylabel("Average distortion")
plt.title("Selecting k with the Elbow Method")
plt.show()

Number of Clusters: 2 	Average Distortion: 4.093218009349731
Number of Clusters: 3 	Average Distortion: 3.8409038095509507
Number of Clusters: 4 	Average Distortion: 3.73086662404098
Number of Clusters: 5 	Average Distortion: 3.6227617453058927
Number of Clusters: 6 	Average Distortion: 3.4897621376752954
Number of Clusters: 7 	Average Distortion: 3.394929716849047
Number of Clusters: 8 	Average Distortion: 3.2797201927385564
Number of Clusters: 9 	Average Distortion: 3.2188238845492423
Number of Clusters: 10 	Average Distortion: 3.1615896791154574


model = KMeans(random_state=1)
visualizer = KElbowVisualizer(model, k=(2, 11), timings=True)
visualizer.fit(EFIndexData_scaled)  # fit the data to the visualizer
visualizer.show()  # finalize and render figure

<matplotlib.axes._subplots.AxesSubplot at 0x7fca2be25810>


sil_score = []
cluster_list = list(range(2, 11))
for n_clusters in cluster_list:
    clusterer = KMeans(n_clusters=n_clusters)
    preds = clusterer.fit_predict((EFIndexData_scaled))
    # centers = clusterer.cluster_centers_
    score = silhouette_score(EFIndexData_scaled, preds)
    sil_score.append(score)
    print("For n_clusters = {}, the silhouette score is {})".format(n_clusters, score))

plt.plot(cluster_list, sil_score)
plt.show()

For n_clusters = 2, the silhouette score is 0.21186865310179862)
For n_clusters = 3, the silhouette score is 0.15389399862337258)
For n_clusters = 4, the silhouette score is 0.1594540534638782)
For n_clusters = 5, the silhouette score is 0.17060819139842529)
For n_clusters = 6, the silhouette score is 0.14371263797754472)
For n_clusters = 7, the silhouette score is 0.15405930576058116)
For n_clusters = 8, the silhouette score is 0.12905730549904978)
For n_clusters = 9, the silhouette score is 0.127997604920279)
For n_clusters = 10, the silhouette score is 0.11979894044745569)


model = KMeans(random_state=1)
visualizer = KElbowVisualizer(model, k=(2, 11), metric="silhouette", timings=True)
visualizer.fit(EFIndexData_scaled)  # fit the data to the visualizer
visualizer.show()  # finalize and render figure

<matplotlib.axes._subplots.AxesSubplot at 0x7fca1fd02a90>


# Finding optimal no. of clusters with silhouette coefficients
visualizer = SilhouetteVisualizer(KMeans(2, random_state=1))
visualizer.fit(EFIndexData_scaled)
visualizer.show()

<matplotlib.axes._subplots.AxesSubplot at 0x7fca1fc7acd0>


# Finding optimal no. of clusters with silhouette coefficients

visualizer = SilhouetteVisualizer(KMeans(3, random_state=1))
visualizer.fit(EFIndexData_scaled)
visualizer.show()

<matplotlib.axes._subplots.AxesSubplot at 0x7fca1fc83ed0>


# Finding optimal no. of clusters with silhouette coefficients
visualizer = SilhouetteVisualizer(KMeans(4, random_state=1))
visualizer.fit(EFIndexData_scaled)
visualizer.show()

<matplotlib.axes._subplots.AxesSubplot at 0x7fca1fcae190>


# Finding optimal no. of clusters with silhouette coefficients
visualizer = SilhouetteVisualizer(KMeans(5, random_state=1))
visualizer.fit(EFIndexData_scaled)
visualizer.show()

<matplotlib.axes._subplots.AxesSubplot at 0x7fca1fcae6d0>


# Finding optimal no. of clusters with silhouette coefficients
visualizer = SilhouetteVisualizer(KMeans(6, random_state=1))
visualizer.fit(EFIndexData_scaled)
visualizer.show()

<matplotlib.axes._subplots.AxesSubplot at 0x7fca1fcf4f10>


# Make a copy of the data set for the evaluation scores

EFIndexData_scaled_Scores = EFIndexData_scaled.copy()

# split dataset in two parts: features and target 

features = EFIndexData_scaled_Scores.drop(['2019_Score'], axis=1)

target = EFIndexData_scaled_Scores['2019_Score']
 
# partition data into training and testing set 

from sklearn.model_selection import train_test_split
feature_train, feature_test, target_train, target_test = train_test_split(features, target, test_size=0.3, random_state=1)


# Import metrics module for performance evaluation

from sklearn.metrics import davies_bouldin_score
from sklearn.metrics import adjusted_rand_score
from sklearn.metrics import fowlkes_mallows_score
 
# Specify the number of clusters
num_clusters = 4
 
# Create and fit the KMeans model
clusterer = KMeans(n_clusters=num_clusters)
clusterer.fit(feature_train)
 
# Predict the target variable
predictions=clusterer.predict(feature_test)
 
# Calculate internal performance evaluation measures
print('Internal performance evaluation measures:')
print('                                        ')
print("Davies-Bouldin Index:", davies_bouldin_score(feature_test, predictions))
print("Silhouette Coefficient:", silhouette_score(feature_test, predictions))
print('                                        ')
# Calculate External performance evaluation measures
print('External performance evaluation measures:')
print('                                        ')
print("Adjusted Rand Score:", adjusted_rand_score(target_test, predictions))
print("Fowlkes Mallows Score:", fowlkes_mallows_score(target_test, predictions))
print('                                        ')

Internal performance evaluation measures:
                                        
Davies-Bouldin Index: 1.172443166805477
Silhouette Coefficient: 0.23167922949483527
                                        
External performance evaluation measures:
                                        
Adjusted Rand Score: 0.00016833954085390232
Fowlkes Mallows Score: 0.03937496154790789

/usr/local/lib/python3.7/dist-packages/sklearn/metrics/cluster/_supervised.py:65: UserWarning: Clustering metrics expects discrete values but received continuous values for label, and multiclass values for target
  warnings.warn(msg, UserWarning)
/usr/local/lib/python3.7/dist-packages/sklearn/metrics/cluster/_supervised.py:65: UserWarning: Clustering metrics expects discrete values but received continuous values for label, and multiclass values for target
  warnings.warn(msg, UserWarning)


# let's take 4 as number of clusters
kmeans = KMeans(n_clusters=4, random_state=0)
kmeans.fit(EFIndexData_scaled)

KMeans(n_clusters=4, random_state=0)


# adding kmeans cluster labels to the original and scaled dataframes

km_EFIndexData["K_means_segments"] = kmeans.labels_
EFIndexData_scaled["K_means_segments"] = kmeans.labels_


# list of distance metrics
distance_metrics = ["euclidean", "chebyshev", "mahalanobis", "cityblock"]

# list of linkage methods
linkage_methods = ["single", "complete", "average", "weighted"]

high_cophenet_corr = 0
high_dm_lm = [0, 0]

for dm in distance_metrics:
    for lm in linkage_methods:
        Z = linkage(EFIndexData_scaled, metric=dm, method=lm)
        c, coph_dists = cophenet(Z, pdist(EFIndexData_scaled))
        print(
            "Cophenetic correlation for {} distance and {} linkage is {}.".format(
                dm.capitalize(), lm, c
            )
        )
        if high_cophenet_corr < c:
            high_cophenet_corr = c
            high_dm_lm[0] = dm
            high_dm_lm[1] = lm

Cophenetic correlation for Euclidean distance and single linkage is 0.7891817411656677.
Cophenetic correlation for Euclidean distance and complete linkage is 0.7474836807497028.
Cophenetic correlation for Euclidean distance and average linkage is 0.8609891664477334.
Cophenetic correlation for Euclidean distance and weighted linkage is 0.7862995372373096.
Cophenetic correlation for Chebyshev distance and single linkage is 0.7804081959099154.
Cophenetic correlation for Chebyshev distance and complete linkage is 0.7175233887755735.
Cophenetic correlation for Chebyshev distance and average linkage is 0.8137296767427991.
Cophenetic correlation for Chebyshev distance and weighted linkage is 0.8079016708635536.
Cophenetic correlation for Mahalanobis distance and single linkage is 0.770129706657455.
Cophenetic correlation for Mahalanobis distance and complete linkage is 0.4588560265047875.
Cophenetic correlation for Mahalanobis distance and average linkage is 0.7588143407821285.
Cophenetic correlation for Mahalanobis distance and weighted linkage is 0.7675149838633272.
Cophenetic correlation for Cityblock distance and single linkage is 0.7722218789994268.
Cophenetic correlation for Cityblock distance and complete linkage is 0.6278410082236408.
Cophenetic correlation for Cityblock distance and average linkage is 0.8268997527346502.
Cophenetic correlation for Cityblock distance and weighted linkage is 0.7712843784170289.


# printing the combination of distance metric and linkage method with the highest cophenetic correlation

print(
    "Highest cophenetic correlation is {}, which is obtained with {} distance and {} linkage.".format(
        high_cophenet_corr, high_dm_lm[0].capitalize(), high_dm_lm[1]
    )
)

Highest cophenetic correlation is 0.8609891664477334, which is obtained with Euclidean distance and average linkage.


# list of linkage methods
linkage_methods = ["single", "complete", "average", "centroid", "ward", "weighted"]

# lists to save results of cophenetic correlation calculation
compare_cols = ["Linkage", "Cophenetic Coefficient"]
compare = []

# to create a subplot image
fig, axs = plt.subplots(len(linkage_methods), 1, figsize=(15, 30))

# We will enumerate through the list of linkage methods above
# For each linkage method, we will plot the dendrogram and calculate the cophenetic correlation
for i, method in enumerate(linkage_methods):
    Z = linkage(EFIndexData_scaled, metric="euclidean", method=method)

    dendrogram(Z, ax=axs[i])
    axs[i].set_title(f"Dendrogram ({method.capitalize()} Linkage)")

    coph_corr, coph_dist = cophenet(Z, pdist(EFIndexData_scaled))
    axs[i].annotate(
        f"Cophenetic\nCorrelation\n{coph_corr:0.2f}",
        (0.80, 0.80),
        xycoords="axes fraction",
    )

    compare.append([method, coph_corr])


hc = AgglomerativeClustering(n_clusters=4, affinity="euclidean", linkage="ward")
hc_labels = hc.fit_predict(EFIndexData_scaled)


hc_EFIndexData["HC_segments"] = hc_labels


pd.crosstab(km_EFIndexData.K_means_segments, km_EFIndexData.Region)


clusters = km_EFIndexData.K_means_segments.unique().tolist()
for cl in clusters:
    print(
        "The",
        km_EFIndexData[km_EFIndexData["K_means_segments"] == cl]["Country_Name"].nunique(),
        "countries in cluster",
        cl,
        "are:",
    )
    print(km_EFIndexData[km_EFIndexData["K_means_segments"] == cl]["Country_Name"].unique())
    print("-" * 100, "\n")

The 73 countries in cluster 2 are:
['Afghanistan' 'Algeria' 'Angola' 'Argentina' 'Bangladesh' 'Belize'
 'Benin' 'Bolivia' 'Burkina Faso' 'Burma' 'Burundi' 'Cambodia' 'Cameroon'
 'Central African Republic' 'Chad' 'Comoros'
 'Congo, Democratic Republic of the Congo' 'Congo, Republic of'
 "Côte d'Ivoire" 'Djibouti' 'Dominican Republic' 'Ecuador' 'Egypt'
 'El Salvador' 'Equatorial Guinea' 'Eritrea' 'Ethiopia' 'Gabon' 'Gambia'
 'Ghana' 'Guatemala' 'Guinea' 'Guinea-Bissau' 'Guyana' 'Haiti' 'Honduras'
 'Iran' 'Kenya' 'Laos' 'Lebanon' 'Liberia' 'Madagascar' 'Malawi' 'Mali'
 'Mauritania' 'Mozambique' 'Nepal' 'Nicaragua' 'Niger' 'Nigeria'
 'Pakistan' 'Papua New Guinea' 'Philippines' 'São Tomé and Príncipe'
 'Senegal' 'Sierra Leone' 'Solomon Islands' 'Sri Lanka' 'Sudan' 'Suriname'
 'Tajikistan' 'Tanzania' 'Timor-Leste' 'Togo' 'Tunisia' 'Turkmenistan'
 'Uganda' 'Ukraine' 'Uzbekistan' 'Venezuela' 'Vietnam' 'Zambia' 'Zimbabwe']
---------------------------------------------------------------------------------------------------- 

The 68 countries in cluster 0 are:
['Albania' 'Armenia' 'Azerbaijan' 'Bahamas' 'Bahrain' 'Barbados' 'Belarus'
 'Bhutan' 'Bosnia and Herzegovina' 'Botswana' 'Brazil' 'Brunei Darussalam'
 'Bulgaria' 'Cabo Verde' 'Chile' 'Colombia' 'Costa Rica' 'Croatia'
 'Cyprus' 'Czech Republic' 'Eswatini' 'Fiji' 'Georgia' 'Greece' 'Hungary'
 'Indonesia' 'Jamaica' 'Jordan' 'Kazakhstan' 'Kuwait' 'Kyrgyz Republic'
 'Latvia' 'Lesotho' 'Lithuania' 'Macau' 'Macedonia' 'Malaysia' 'Maldives'
 'Mauritius' 'Mexico' 'Moldova' 'Mongolia' 'Montenegro' 'Morocco'
 'Namibia' 'Oman' 'Panama' 'Paraguay' 'Peru' 'Poland' 'Qatar' 'Romania'
 'Russia' 'Rwanda' 'Saint Lucia' 'Saint Vincent and the Grenadines'
 'Samoa' 'Saudi Arabia' 'Serbia' 'Slovakia' 'South Africa' 'Thailand'
 'Tonga' 'Trinidad and Tobago' 'Turkey' 'United Arab Emirates' 'Uruguay'
 'Vanuatu']
---------------------------------------------------------------------------------------------------- 

The 29 countries in cluster 1 are:
['Australia' 'Austria' 'Belgium' 'Canada' 'Denmark' 'Estonia' 'Finland'
 'France' 'Germany' 'Hong Kong' 'Iceland' 'Ireland' 'Israel' 'Italy'
 'Japan' 'Korea, South' 'Luxembourg' 'Malta' 'Netherlands' 'New Zealand'
 'Norway' 'Portugal' 'Singapore' 'Slovenia' 'Spain' 'Sweden' 'Switzerland'
 'Taiwan ' 'United Kingdom']
---------------------------------------------------------------------------------------------------- 

The 3 countries in cluster 3 are:
['China' 'India' 'United States']
----------------------------------------------------------------------------------------------------


km_EFIndexData_profiling = km_EFIndexData.groupby("K_means_segments")[col_for_clustering].mean()
km_EFIndexData_profiling["count_in_each_segment"] = (
    km_EFIndexData.groupby("K_means_segments")["2019_Score"].count().values
)

# displaying the group-wise means of variables
km_EFIndexData_profiling.style.highlight_max(color="green", axis=0)


# let's plot the attributes of only the big clusters
c1 = [0, 1, 2]
km_EFIndexData2 = km_EFIndexData[km_EFIndexData.K_means_segments.isin(c1)]


fig, axes = plt.subplots(7, 4, figsize=(25, 35))
counter = 0

for ii in range(7):
    for jj in range(4):
        if counter < 26:
            sns.boxplot(
                ax=axes[ii][jj],
                y=km_EFIndexData2[col_for_clustering[counter]],
                x=km_EFIndexData2["K_means_segments"],
            )
            counter = counter + 1

fig.tight_layout(pad=3.0)


pd.crosstab(hc_EFIndexData.HC_segments, hc_EFIndexData.Region)


clusters = hc_EFIndexData.HC_segments.unique().tolist()
for cl in clusters:
    print(
        "The",
        hc_EFIndexData[hc_EFIndexData["HC_segments"] == cl]["Country_Name"].nunique(),
        "countries in cluster",
        cl,
        "are:",
    )
    print(hc_EFIndexData[hc_EFIndexData["HC_segments"] == cl]["Country_Name"].unique())
    print("-" * 100, "\n")

The 76 countries in cluster 0 are:
['Afghanistan' 'Algeria' 'Angola' 'Argentina' 'Bangladesh' 'Belize'
 'Benin' 'Bolivia' 'Brazil' 'Burkina Faso' 'Burma' 'Burundi' 'Cambodia'
 'Cameroon' 'Central African Republic' 'Chad' 'Comoros'
 'Congo, Democratic Republic of the Congo' 'Congo, Republic of'
 "Côte d'Ivoire" 'Djibouti' 'Dominican Republic' 'Ecuador' 'Egypt'
 'El Salvador' 'Equatorial Guinea' 'Eritrea' 'Ethiopia' 'Gabon' 'Gambia'
 'Ghana' 'Guatemala' 'Guinea' 'Guinea-Bissau' 'Guyana' 'Haiti' 'Honduras'
 'Iran' 'Kenya' 'Laos' 'Lebanon' 'Liberia' 'Madagascar' 'Malawi' 'Mali'
 'Mauritania' 'Moldova' 'Mozambique' 'Nepal' 'Nicaragua' 'Niger' 'Nigeria'
 'Pakistan' 'Papua New Guinea' 'Paraguay' 'Philippines'
 'São Tomé and Príncipe' 'Senegal' 'Sierra Leone' 'Solomon Islands'
 'Sri Lanka' 'Sudan' 'Suriname' 'Tajikistan' 'Tanzania' 'Timor-Leste'
 'Togo' 'Tunisia' 'Turkmenistan' 'Uganda' 'Ukraine' 'Uzbekistan'
 'Venezuela' 'Vietnam' 'Zambia' 'Zimbabwe']
---------------------------------------------------------------------------------------------------- 

The 62 countries in cluster 2 are:
['Albania' 'Armenia' 'Azerbaijan' 'Bahamas' 'Bahrain' 'Barbados' 'Belarus'
 'Bhutan' 'Bosnia and Herzegovina' 'Botswana' 'Brunei Darussalam'
 'Bulgaria' 'Cabo Verde' 'Colombia' 'Costa Rica' 'Czech Republic'
 'Eswatini' 'Fiji' 'Georgia' 'Hungary' 'Indonesia' 'Jamaica' 'Jordan'
 'Kazakhstan' 'Kuwait' 'Kyrgyz Republic' 'Latvia' 'Lesotho' 'Lithuania'
 'Macau' 'Macedonia' 'Malaysia' 'Maldives' 'Malta' 'Mauritius' 'Mexico'
 'Mongolia' 'Montenegro' 'Morocco' 'Namibia' 'Oman' 'Panama' 'Peru'
 'Poland' 'Qatar' 'Romania' 'Russia' 'Rwanda' 'Saint Lucia'
 'Saint Vincent and the Grenadines' 'Samoa' 'Saudi Arabia' 'Serbia'
 'Slovakia' 'South Africa' 'Thailand' 'Tonga' 'Trinidad and Tobago'
 'Turkey' 'United Arab Emirates' 'Uruguay' 'Vanuatu']
---------------------------------------------------------------------------------------------------- 

The 32 countries in cluster 1 are:
['Australia' 'Austria' 'Belgium' 'Canada' 'Chile' 'Croatia' 'Cyprus'
 'Denmark' 'Estonia' 'Finland' 'France' 'Germany' 'Greece' 'Hong Kong'
 'Iceland' 'Ireland' 'Israel' 'Italy' 'Japan' 'Korea, South' 'Luxembourg'
 'Netherlands' 'New Zealand' 'Norway' 'Portugal' 'Singapore' 'Slovenia'
 'Spain' 'Sweden' 'Switzerland' 'Taiwan ' 'United Kingdom']
---------------------------------------------------------------------------------------------------- 

The 3 countries in cluster 3 are:
['China' 'India' 'United States']
----------------------------------------------------------------------------------------------------


hc_EFIndexData_profiling = hc_EFIndexData.groupby("HC_segments")[col_for_clustering].mean()
hc_EFIndexData_profiling["count_in_each_segment"] = (
    hc_EFIndexData.groupby("HC_segments")["2019_Score"].count().values
)

# displaying the group-wise means of variables
hc_EFIndexData_profiling.style.highlight_max(color="blue", axis=0)


# Let's plot the attributes of only the big clusters
c1 = [0, 1, 2]
hc_EFIndexData2 = hc_EFIndexData[hc_EFIndexData.HC_segments.isin(c1)]


fig, axes = plt.subplots(7, 4, figsize=(25, 35))
counter = 0

for ii in range(7):
    for jj in range(4):
        if counter < 26:
            sns.boxplot(
                ax=axes[ii][jj],
                y=hc_EFIndexData2[col_for_clustering[counter]],
                x=hc_EFIndexData2["HC_segments"],
            )
            counter = counter + 1

fig.tight_layout(pad=2.0)


km_EFIndexData_profiling.style.highlight_max(color="green", axis=0)


clusters = km_EFIndexData.K_means_segments.unique().tolist()
for cl in clusters:
    print(
        "The",
        km_EFIndexData[km_EFIndexData["K_means_segments"] == cl]["Country_Name"].nunique(),
        "countries in cluster",
        cl,
        "are:",
    )
    print(km_EFIndexData[km_EFIndexData["K_means_segments"] == cl]["Country_Name"].unique())
    print("-" * 100, "\n")

The 73 countries in cluster 2 are:
['Afghanistan' 'Algeria' 'Angola' 'Argentina' 'Bangladesh' 'Belize'
 'Benin' 'Bolivia' 'Burkina Faso' 'Burma' 'Burundi' 'Cambodia' 'Cameroon'
 'Central African Republic' 'Chad' 'Comoros'
 'Congo, Democratic Republic of the Congo' 'Congo, Republic of'
 "Côte d'Ivoire" 'Djibouti' 'Dominican Republic' 'Ecuador' 'Egypt'
 'El Salvador' 'Equatorial Guinea' 'Eritrea' 'Ethiopia' 'Gabon' 'Gambia'
 'Ghana' 'Guatemala' 'Guinea' 'Guinea-Bissau' 'Guyana' 'Haiti' 'Honduras'
 'Iran' 'Kenya' 'Laos' 'Lebanon' 'Liberia' 'Madagascar' 'Malawi' 'Mali'
 'Mauritania' 'Mozambique' 'Nepal' 'Nicaragua' 'Niger' 'Nigeria'
 'Pakistan' 'Papua New Guinea' 'Philippines' 'São Tomé and Príncipe'
 'Senegal' 'Sierra Leone' 'Solomon Islands' 'Sri Lanka' 'Sudan' 'Suriname'
 'Tajikistan' 'Tanzania' 'Timor-Leste' 'Togo' 'Tunisia' 'Turkmenistan'
 'Uganda' 'Ukraine' 'Uzbekistan' 'Venezuela' 'Vietnam' 'Zambia' 'Zimbabwe']
---------------------------------------------------------------------------------------------------- 

The 68 countries in cluster 0 are:
['Albania' 'Armenia' 'Azerbaijan' 'Bahamas' 'Bahrain' 'Barbados' 'Belarus'
 'Bhutan' 'Bosnia and Herzegovina' 'Botswana' 'Brazil' 'Brunei Darussalam'
 'Bulgaria' 'Cabo Verde' 'Chile' 'Colombia' 'Costa Rica' 'Croatia'
 'Cyprus' 'Czech Republic' 'Eswatini' 'Fiji' 'Georgia' 'Greece' 'Hungary'
 'Indonesia' 'Jamaica' 'Jordan' 'Kazakhstan' 'Kuwait' 'Kyrgyz Republic'
 'Latvia' 'Lesotho' 'Lithuania' 'Macau' 'Macedonia' 'Malaysia' 'Maldives'
 'Mauritius' 'Mexico' 'Moldova' 'Mongolia' 'Montenegro' 'Morocco'
 'Namibia' 'Oman' 'Panama' 'Paraguay' 'Peru' 'Poland' 'Qatar' 'Romania'
 'Russia' 'Rwanda' 'Saint Lucia' 'Saint Vincent and the Grenadines'
 'Samoa' 'Saudi Arabia' 'Serbia' 'Slovakia' 'South Africa' 'Thailand'
 'Tonga' 'Trinidad and Tobago' 'Turkey' 'United Arab Emirates' 'Uruguay'
 'Vanuatu']
---------------------------------------------------------------------------------------------------- 

The 29 countries in cluster 1 are:
['Australia' 'Austria' 'Belgium' 'Canada' 'Denmark' 'Estonia' 'Finland'
 'France' 'Germany' 'Hong Kong' 'Iceland' 'Ireland' 'Israel' 'Italy'
 'Japan' 'Korea, South' 'Luxembourg' 'Malta' 'Netherlands' 'New Zealand'
 'Norway' 'Portugal' 'Singapore' 'Slovenia' 'Spain' 'Sweden' 'Switzerland'
 'Taiwan ' 'United Kingdom']
---------------------------------------------------------------------------------------------------- 

The 3 countries in cluster 3 are:
['China' 'India' 'United States']
----------------------------------------------------------------------------------------------------


hc_EFIndexData_profiling.style.highlight_max(color="blue", axis=0)


clusters = hc_EFIndexData.HC_segments.unique().tolist()
for cl in clusters:
    print(
        "The",
        hc_EFIndexData[hc_EFIndexData["HC_segments"] == cl]["Country_Name"].nunique(),
        "countries in cluster",
        cl,
        "are:",
    )
    print(hc_EFIndexData[hc_EFIndexData["HC_segments"] == cl]["Country_Name"].unique())
    print("-" * 100, "\n")

The 76 countries in cluster 0 are:
['Afghanistan' 'Algeria' 'Angola' 'Argentina' 'Bangladesh' 'Belize'
 'Benin' 'Bolivia' 'Brazil' 'Burkina Faso' 'Burma' 'Burundi' 'Cambodia'
 'Cameroon' 'Central African Republic' 'Chad' 'Comoros'
 'Congo, Democratic Republic of the Congo' 'Congo, Republic of'
 "Côte d'Ivoire" 'Djibouti' 'Dominican Republic' 'Ecuador' 'Egypt'
 'El Salvador' 'Equatorial Guinea' 'Eritrea' 'Ethiopia' 'Gabon' 'Gambia'
 'Ghana' 'Guatemala' 'Guinea' 'Guinea-Bissau' 'Guyana' 'Haiti' 'Honduras'
 'Iran' 'Kenya' 'Laos' 'Lebanon' 'Liberia' 'Madagascar' 'Malawi' 'Mali'
 'Mauritania' 'Moldova' 'Mozambique' 'Nepal' 'Nicaragua' 'Niger' 'Nigeria'
 'Pakistan' 'Papua New Guinea' 'Paraguay' 'Philippines'
 'São Tomé and Príncipe' 'Senegal' 'Sierra Leone' 'Solomon Islands'
 'Sri Lanka' 'Sudan' 'Suriname' 'Tajikistan' 'Tanzania' 'Timor-Leste'
 'Togo' 'Tunisia' 'Turkmenistan' 'Uganda' 'Ukraine' 'Uzbekistan'
 'Venezuela' 'Vietnam' 'Zambia' 'Zimbabwe']
---------------------------------------------------------------------------------------------------- 

The 62 countries in cluster 2 are:
['Albania' 'Armenia' 'Azerbaijan' 'Bahamas' 'Bahrain' 'Barbados' 'Belarus'
 'Bhutan' 'Bosnia and Herzegovina' 'Botswana' 'Brunei Darussalam'
 'Bulgaria' 'Cabo Verde' 'Colombia' 'Costa Rica' 'Czech Republic'
 'Eswatini' 'Fiji' 'Georgia' 'Hungary' 'Indonesia' 'Jamaica' 'Jordan'
 'Kazakhstan' 'Kuwait' 'Kyrgyz Republic' 'Latvia' 'Lesotho' 'Lithuania'
 'Macau' 'Macedonia' 'Malaysia' 'Maldives' 'Malta' 'Mauritius' 'Mexico'
 'Mongolia' 'Montenegro' 'Morocco' 'Namibia' 'Oman' 'Panama' 'Peru'
 'Poland' 'Qatar' 'Romania' 'Russia' 'Rwanda' 'Saint Lucia'
 'Saint Vincent and the Grenadines' 'Samoa' 'Saudi Arabia' 'Serbia'
 'Slovakia' 'South Africa' 'Thailand' 'Tonga' 'Trinidad and Tobago'
 'Turkey' 'United Arab Emirates' 'Uruguay' 'Vanuatu']
---------------------------------------------------------------------------------------------------- 

The 32 countries in cluster 1 are:
['Australia' 'Austria' 'Belgium' 'Canada' 'Chile' 'Croatia' 'Cyprus'
 'Denmark' 'Estonia' 'Finland' 'France' 'Germany' 'Greece' 'Hong Kong'
 'Iceland' 'Ireland' 'Israel' 'Italy' 'Japan' 'Korea, South' 'Luxembourg'
 'Netherlands' 'New Zealand' 'Norway' 'Portugal' 'Singapore' 'Slovenia'
 'Spain' 'Sweden' 'Switzerland' 'Taiwan ' 'United Kingdom']
---------------------------------------------------------------------------------------------------- 

The 3 countries in cluster 3 are:
['China' 'India' 'United States']
----------------------------------------------------------------------------------------------------


km_EFIndexData["K_means_segments"] = km_EFIndexData["K_means_segments"].replace({0: 2, 2: 0})

km_EFIndexData_profiling = km_EFIndexData.groupby("K_means_segments")[col_for_clustering].mean()
km_EFIndexData_profiling["count_in_each_segment"] = (
    km_EFIndexData.groupby("K_means_segments")["2019_Score"].count().values
)


km_EFIndexData_profiling.style.highlight_max(color="green", axis=0)


clusters = km_EFIndexData.K_means_segments.unique().tolist()
for cl in clusters:
    print(
        "The",
        km_EFIndexData[km_EFIndexData["K_means_segments"] == cl]["Country_Name"].nunique(),
        "countries in cluster",
        cl,
        "are:",
    )
    print(km_EFIndexData[km_EFIndexData["K_means_segments"] == cl]["Country_Name"].unique())
    print("-" * 100, "\n")

The 73 countries in cluster 0 are:
['Afghanistan' 'Algeria' 'Angola' 'Argentina' 'Bangladesh' 'Belize'
 'Benin' 'Bolivia' 'Burkina Faso' 'Burma' 'Burundi' 'Cambodia' 'Cameroon'
 'Central African Republic' 'Chad' 'Comoros'
 'Congo, Democratic Republic of the Congo' 'Congo, Republic of'
 "Côte d'Ivoire" 'Djibouti' 'Dominican Republic' 'Ecuador' 'Egypt'
 'El Salvador' 'Equatorial Guinea' 'Eritrea' 'Ethiopia' 'Gabon' 'Gambia'
 'Ghana' 'Guatemala' 'Guinea' 'Guinea-Bissau' 'Guyana' 'Haiti' 'Honduras'
 'Iran' 'Kenya' 'Laos' 'Lebanon' 'Liberia' 'Madagascar' 'Malawi' 'Mali'
 'Mauritania' 'Mozambique' 'Nepal' 'Nicaragua' 'Niger' 'Nigeria'
 'Pakistan' 'Papua New Guinea' 'Philippines' 'São Tomé and Príncipe'
 'Senegal' 'Sierra Leone' 'Solomon Islands' 'Sri Lanka' 'Sudan' 'Suriname'
 'Tajikistan' 'Tanzania' 'Timor-Leste' 'Togo' 'Tunisia' 'Turkmenistan'
 'Uganda' 'Ukraine' 'Uzbekistan' 'Venezuela' 'Vietnam' 'Zambia' 'Zimbabwe']
---------------------------------------------------------------------------------------------------- 

The 68 countries in cluster 2 are:
['Albania' 'Armenia' 'Azerbaijan' 'Bahamas' 'Bahrain' 'Barbados' 'Belarus'
 'Bhutan' 'Bosnia and Herzegovina' 'Botswana' 'Brazil' 'Brunei Darussalam'
 'Bulgaria' 'Cabo Verde' 'Chile' 'Colombia' 'Costa Rica' 'Croatia'
 'Cyprus' 'Czech Republic' 'Eswatini' 'Fiji' 'Georgia' 'Greece' 'Hungary'
 'Indonesia' 'Jamaica' 'Jordan' 'Kazakhstan' 'Kuwait' 'Kyrgyz Republic'
 'Latvia' 'Lesotho' 'Lithuania' 'Macau' 'Macedonia' 'Malaysia' 'Maldives'
 'Mauritius' 'Mexico' 'Moldova' 'Mongolia' 'Montenegro' 'Morocco'
 'Namibia' 'Oman' 'Panama' 'Paraguay' 'Peru' 'Poland' 'Qatar' 'Romania'
 'Russia' 'Rwanda' 'Saint Lucia' 'Saint Vincent and the Grenadines'
 'Samoa' 'Saudi Arabia' 'Serbia' 'Slovakia' 'South Africa' 'Thailand'
 'Tonga' 'Trinidad and Tobago' 'Turkey' 'United Arab Emirates' 'Uruguay'
 'Vanuatu']
---------------------------------------------------------------------------------------------------- 

The 29 countries in cluster 1 are:
['Australia' 'Austria' 'Belgium' 'Canada' 'Denmark' 'Estonia' 'Finland'
 'France' 'Germany' 'Hong Kong' 'Iceland' 'Ireland' 'Israel' 'Italy'
 'Japan' 'Korea, South' 'Luxembourg' 'Malta' 'Netherlands' 'New Zealand'
 'Norway' 'Portugal' 'Singapore' 'Slovenia' 'Spain' 'Sweden' 'Switzerland'
 'Taiwan ' 'United Kingdom']
---------------------------------------------------------------------------------------------------- 

The 3 countries in cluster 3 are:
['China' 'India' 'United States']
----------------------------------------------------------------------------------------------------


pd.crosstab(hc_EFIndexData.HC_segments, EFIndexData.Region).style.highlight_max(
    color="blue", axis=0
)


pd.crosstab(hc_EFIndexData.HC_segments, hc_EFIndexData['Government_Integrity']).style.highlight_max(
    color="green", axis=0
)


pd.crosstab(hc_EFIndexData.HC_segments, hc_EFIndexData['Property_Rights']).style.highlight_max(
    color="yellow", axis=0
)


pd.crosstab(hc_EFIndexData.HC_segments, hc_EFIndexData['Judical_Effectiveness']).style.highlight_max(
    color="grey", axis=0
)


pd.crosstab(hc_EFIndexData.HC_segments, hc_EFIndexData['Tax_Burden']).style.highlight_max(
    color="orange", axis=0
)


pd.crosstab(hc_EFIndexData.HC_segments, hc_EFIndexData['Fiscal_Health']).style.highlight_max(
    color="cyan", axis=0
)


pd.crosstab(hc_EFIndexData.HC_segments, hc_EFIndexData['Labor_Freedom']).style.highlight_max(
    color="pink", axis=0
)


pd.crosstab(hc_EFIndexData.HC_segments, hc_EFIndexData['Monetary_Freedom']).style.highlight_max(
    color="yellow", axis=0
)


pd.crosstab(hc_EFIndexData.HC_segments, hc_EFIndexData['Trade_Freedom']).style.highlight_max(
    color="purple", axis=0
)


pd.crosstab(hc_EFIndexData.HC_segments, hc_EFIndexData['Investment_Freedom']).style.highlight_max(
    color="red", axis=0
)


pd.crosstab(hc_EFIndexData.HC_segments, hc_EFIndexData['Financial_Freedom']).style.highlight_max(
    color="blue", axis=0
)


pd.crosstab(hc_EFIndexData.HC_segments, hc_EFIndexData['Income_Tax_Rate_Percent']).style.highlight_max(
    color="brown", axis=0
)


pd.crosstab(hc_EFIndexData.HC_segments, hc_EFIndexData['Corporate_Tax_Rate_Percent']).style.highlight_max(
    color="orange", axis=0
)


pd.crosstab(hc_EFIndexData.HC_segments, hc_EFIndexData['Tax_Burden_Percent_of_GDP']).style.highlight_max(
    color="red", axis=0
)


pd.crosstab(hc_EFIndexData.HC_segments, hc_EFIndexData['Government_Expenditure_Percent_of_GDP']).style.highlight_max(
    color="pink", axis=0
)


pd.crosstab(hc_EFIndexData.HC_segments, hc_EFIndexData['Population_in_Millions']).style.highlight_max(
    color="lightblue", axis=0
)


pd.crosstab(hc_EFIndexData.HC_segments, hc_EFIndexData['GDP_in_Billions_by_PPP_in_USA_Dollars']).style.highlight_max(
    color="green", axis=0
)


pd.crosstab(hc_EFIndexData.HC_segments, hc_EFIndexData['Unemployment_Percent']).style.highlight_max(
    color="red", axis=0
)


pd.crosstab(hc_EFIndexData.HC_segments, hc_EFIndexData['Inflation_Percent']).style.highlight_max(
    color="teal", axis=0
)


pd.crosstab(hc_EFIndexData.HC_segments, hc_EFIndexData['FDI_Inflow_in_Millions']).style.highlight_max(
    color="yellow", axis=0
)


pd.crosstab(hc_EFIndexData.HC_segments, hc_EFIndexData['Public_Debt_by_Percent_of_GDP']).style.highlight_max(
    color="green", axis=0
)


hc_EFIndexData.groupby(hc_EFIndexData.HC_segments)[['2019_Score','GDP_Growth_Rate_Percent','Inflation_Percent', 'Property_Rights', 'Trade_Freedom','Fiscal_Health','Judical_Effectiveness']].mean()


hc_EFIndexData.groupby(hc_EFIndexData.HC_segments)[['2019_Score','Business_Freedom','Investment_Freedom','Labor_Freedom','Government_Integrity','Financial_Freedom','Income_Tax_Rate_Percent']].mean()


data_subset=hc_EFIndexData[['HC_segments','Country_Name','2019_Score']]
sort=data_subset.groupby(['HC_segments'])['2019_Score'].transform(max) == data_subset['2019_Score']
data_subset[sort]


data_subset=hc_EFIndexData[['HC_segments','Country_Name','2019_Score']]
sort=data_subset.groupby(['HC_segments'])['2019_Score'].transform(min) == data_subset['2019_Score']
data_subset[sort]


# Let us see the names of the countries in each cluster
for cl in hc_EFIndexData["HC_segments"].unique():
    print("In cluster {}, the following countries are present:".format(cl))
    print(hc_EFIndexData[hc_EFIndexData["HC_segments"] == cl]["Country_Name"].unique())
    print()

In cluster 0, the following countries are present:
['Afghanistan' 'Algeria' 'Angola' 'Argentina' 'Bangladesh' 'Belize'
 'Benin' 'Bolivia' 'Brazil' 'Burkina Faso' 'Burma' 'Burundi' 'Cambodia'
 'Cameroon' 'Central African Republic' 'Chad' 'Comoros'
 'Congo, Democratic Republic of the Congo' 'Congo, Republic of'
 "Côte d'Ivoire" 'Djibouti' 'Dominican Republic' 'Ecuador' 'Egypt'
 'El Salvador' 'Equatorial Guinea' 'Eritrea' 'Ethiopia' 'Gabon' 'Gambia'
 'Ghana' 'Guatemala' 'Guinea' 'Guinea-Bissau' 'Guyana' 'Haiti' 'Honduras'
 'Iran' 'Kenya' 'Laos' 'Lebanon' 'Liberia' 'Madagascar' 'Malawi' 'Mali'
 'Mauritania' 'Moldova' 'Mozambique' 'Nepal' 'Nicaragua' 'Niger' 'Nigeria'
 'Pakistan' 'Papua New Guinea' 'Paraguay' 'Philippines'
 'São Tomé and Príncipe' 'Senegal' 'Sierra Leone' 'Solomon Islands'
 'Sri Lanka' 'Sudan' 'Suriname' 'Tajikistan' 'Tanzania' 'Timor-Leste'
 'Togo' 'Tunisia' 'Turkmenistan' 'Uganda' 'Ukraine' 'Uzbekistan'
 'Venezuela' 'Vietnam' 'Zambia' 'Zimbabwe']

In cluster 2, the following countries are present:
['Albania' 'Armenia' 'Azerbaijan' 'Bahamas' 'Bahrain' 'Barbados' 'Belarus'
 'Bhutan' 'Bosnia and Herzegovina' 'Botswana' 'Brunei Darussalam'
 'Bulgaria' 'Cabo Verde' 'Colombia' 'Costa Rica' 'Czech Republic'
 'Eswatini' 'Fiji' 'Georgia' 'Hungary' 'Indonesia' 'Jamaica' 'Jordan'
 'Kazakhstan' 'Kuwait' 'Kyrgyz Republic' 'Latvia' 'Lesotho' 'Lithuania'
 'Macau' 'Macedonia' 'Malaysia' 'Maldives' 'Malta' 'Mauritius' 'Mexico'
 'Mongolia' 'Montenegro' 'Morocco' 'Namibia' 'Oman' 'Panama' 'Peru'
 'Poland' 'Qatar' 'Romania' 'Russia' 'Rwanda' 'Saint Lucia'
 'Saint Vincent and the Grenadines' 'Samoa' 'Saudi Arabia' 'Serbia'
 'Slovakia' 'South Africa' 'Thailand' 'Tonga' 'Trinidad and Tobago'
 'Turkey' 'United Arab Emirates' 'Uruguay' 'Vanuatu']

In cluster 1, the following countries are present:
['Australia' 'Austria' 'Belgium' 'Canada' 'Chile' 'Croatia' 'Cyprus'
 'Denmark' 'Estonia' 'Finland' 'France' 'Germany' 'Greece' 'Hong Kong'
 'Iceland' 'Ireland' 'Israel' 'Italy' 'Japan' 'Korea, South' 'Luxembourg'
 'Netherlands' 'New Zealand' 'Norway' 'Portugal' 'Singapore' 'Slovenia'
 'Spain' 'Sweden' 'Switzerland' 'Taiwan ' 'United Kingdom']

In cluster 3, the following countries are present:
['China' 'India' 'United States']

	CountryID	Country Name	WEBNAME	Region	World Rank	Region Rank	2019 Score	Property Rights	Judical Effectiveness	Government Integrity	Tax Burden	Gov't Spending	Fiscal Health	Business Freedom	Labor Freedom	Monetary Freedom	Trade Freedom	Investment Freedom	Financial Freedom	Tariff Rate (%)	Income Tax Rate (%)	Corporate Tax Rate (%)	Tax Burden % of GDP	Gov't Expenditure % of GDP	Population (Millions)	GDP (Billions, PPP)	GDP Growth Rate (%)	5 Year GDP Growth Rate (%)	GDP per Capita (PPP)	Unemployment (%)	Inflation (%)	FDI Inflow (Millions)	Public Debt (% of GDP)
Country
Benin	17	Benin	Benin	Sub-Saharan Africa	127.0	21.0	55.3	37.2	32.8	28.1	69.3	83.4	27.9	62.4	53.8	86.4	61.8	70.0	50.0	11.6	45.0	30.0	11.9	23.5	11.1	$25.3	5.6	5.0	$2,277	2.5	0.1	184.4	54.6
Vanuatu	178	Vanuatu	Vanuatu	Asia-Pacific	116.0	26.0	56.4	65.9	36.4	51.9	97.3	54.1	15.3	52.4	58.8	75.0	64.4	65.0	40.0	7.8	0.0	0.0	16.3	39.1	0.3	$0.8	4.2	2.4	$2,739	5.2	3.1	24.7	48.4
Guatemala	65	Guatemala	Guatemala	Americas	77.0	16.0	62.6	40.3	32.3	26.4	79.2	95.6	96.2	53.6	48.7	77.0	82.2	70.0	50.0	1.4	31.0	31.0	12.6	12.1	16.9	$137.8	2.8	3.6	$8,145	2.7	4.4	1,146.7	24.4
Côte d'Ivoire	40	Côte d'Ivoire	CoeDivoire	Sub-Saharan Africa	78.0	5.0	62.4	40.9	47.8	38.1	77.5	83.9	74.3	61.0	52.5	74.2	73.6	75.0	50.0	8.2	36.0	25.0	18.2	23.1	25.0	$96.9	7.8	8.6	$3,883	2.6	0.8	674.7	46.4
Togo	165	Togo	Togo	Sub-Saharan Africa	158.0	35.0	50.3	35.5	29.6	28.1	67.8	77.0	24.5	50.4	46.7	79.1	69.4	65.0	30.0	10.3	45.0	27.0	21.5	27.7	7.8	$12.9	4.4	5.5	$1,659	1.8	-0.7	145.6	78.6
Tunisia	168	Tunisia	Tunisia	Middle East and North Africa	125.0	10.0	55.4	49.2	42.7	36.6	74.4	74.4	37.9	76.7	50.3	76.0	71.4	45.0	30.0	9.3	35.0	30.0	20.8	29.2	11.5	$135.4	1.9	1.7	$11,755	15.4	5.3	879.5	71.3
Liberia	95	Liberia	Liberia	Sub-Saharan Africa	160.0	37.0	49.7	26.7	39.0	24.2	82.7	62.1	69.1	50.6	38.3	68.9	60.1	55.0	20.0	12.4	25.0	25.0	21.8	35.5	4.5	$6.1	2.5	2.1	$1,354	2.4	12.4	247.8	34.4
Papua New Guinea	128	Papua New Guinea	PapuaNewGuinea	Asia-Pacific	101.0	21.0	58.4	37.4	49.0	37.2	71.8	89.1	75.2	62.2	72.6	70.0	80.9	25.0	30.0	2.0	42.0	30.0	12.5	19.1	8.3	$30.3	2.5	5.8	$3,675	2.7	5.2	-200.5	32.6
Colombia	35	Colombia	Colombia	Americas	49.0	8.0	67.3	59.2	34.3	33.5	74.3	75.0	79.2	71.4	78.5	75.6	76.0	80.0	70.0	7.0	33.0	33.0	19.9	28.9	49.3	$714.0	1.8	3.2	$14,485	8.9	4.3	14,518.0	49.4
Estonia	54	Estonia	Estonia	Europe	15.0	7.0	76.6	81.5	76.0	73.1	79.9	51.1	99.8	75.3	57.2	79.6	86.0	90.0	70.0	2.0	20.0	20.0	34.7	40.4	1.3	$41.6	4.9	2.7	$31,750	5.8	3.7	784.4	8.8
Morocco	115	Morocco	Morocco	Middle East and North Africa	75.0	6.0	62.9	57.2	47.1	39.2	72.2	72.7	66.9	70.3	33.1	83.5	77.4	65.0	70.0	3.8	38.0	30.0	20.9	30.2	34.9	$298.6	4.2	3.4	$8,567	9.3	0.8	2,651.4	64.4
Ethiopia	55	Ethiopia	Ethiopia	Sub-Saharan Africa	137.0	26.0	53.6	32.6	40.9	35.1	77.2	90.4	83.3	48.6	58.0	60.8	60.8	35.0	20.0	12.1	35.0	30.0	12.4	17.9	92.7	$200.2	10.9	9.9	$2,161	5.2	9.9	3,586.4	56.2
Argentina	5	Argentina	Argentina	Americas	148.0	26.0	52.2	47.8	44.5	33.5	69.3	49.5	33.0	56.4	46.9	60.2	70.0	55.0	60.0	7.5	35.0	30.0	30.8	41.0	44.1	$920.2	2.9	0.7	$20,876	8.7	25.7	11,857.0	52.6
New Zealand	120	New Zealand	NewZealand	Asia-Pacific	3.0	3.0	84.4	95.0	83.5	96.7	71.0	50.4	98.6	91.0	86.7	87.5	92.4	80.0	80.0	1.3	33.0	28.0	32.1	40.7	4.8	$188.6	3.0	3.3	$38,934	4.9	1.9	3,572.0	26.4
Italy	80	Italy	Italy	Europe	80.0	36.0	62.2	71.7	49.8	43.7	55.6	26.5	71.3	71.7	51.1	84.0	86.0	85.0	50.0	2.0	43.0	27.5	42.9	49.5	60.6	$2,310.9	1.5	0.3	$38,140	11.2	1.3	17,077.1	131.5
Central African Republic	31	Central African Republic	CentralAfricanRepublic	Sub-Saharan Africa	161.0	38.0	49.1	19.6	29.6	23.2	65.2	94.2	94.3	24.2	40.1	72.3	51.0	45.0	30.0	14.5	50.0	30.0	9.0	13.9	5.0	$3.4	4.0	-4.5	$677	6.0	3.8	17.2	53.4
Dominica	47	Dominica	Dominica	Americas	72.0	14.0	63.6	49.2	63.8	54.5	72.1	53.5	84.7	70.7	60.4	85.7	68.2	70.0	30.0	8.4	35.0	30.0	25.8	39.4	0.1	$0.8	-4.2	0.0	$11,102	NaN	0.6	18.9	87.6
Oman	125	Oman	Oman	Middle East and North Africa	88.0	7.0	61.0	58.1	51.6	53.8	97.8	32.5	16.1	75.2	57.3	77.7	87.0	65.0	60.0	1.5	0.0	12.0	8.5	47.4	4.1	$186.6	-0.3	2.7	$45,157	3.3	1.6	1,867.4	44.2
Malaysia	104	Malaysia	Malaysia	Asia-Pacific	22.0	6.0	74.0	84.1	68.2	55.4	85.6	83.2	82.4	83.9	74.4	78.6	82.0	60.0	50.0	4.0	25.0	25.0	13.8	23.7	32.1	$930.8	5.9	5.2	$29,041	3.4	3.8	9,543.4	54.2
Malawi	103	Malawi	Malawi	Sub-Saharan Africa	153.0	32.0	51.4	35.8	40.1	25.2	79.8	70.3	19.1	41.7	64.0	65.5	75.4	50.0	50.0	4.8	30.0	30.0	14.8	31.5	19.2	$22.4	4.0	4.0	$1,167	5.9	11.5	277.1	59.3
Dominican Republic	48	Dominican Republic	DominicanRepublic	Americas	89.0	19.0	61.0	50.6	18.1	23.2	84.6	90.3	89.9	51.9	57.6	79.7	75.8	70.0	40.0	4.6	25.0	27.0	13.6	17.9	10.2	$172.4	4.6	6.1	$16,944	5.5	3.3	3,570.0	37.7
Bangladesh	12	Bangladesh	Bangladesh	Asia-Pacific	121.0	27.0	55.6	36.1	34.5	24.4	72.7	94.5	77.6	50.9	68.2	69.9	63.6	45.0	30.0	10.7	25.0	45.0	8.8	13.6	163.2	$687.1	7.1	6.7	$4,211	4.4	5.7	2,151.6	32.4
Mali	106	Mali	Mali	Sub-Saharan Africa	103.0	12.0	58.1	33.7	33.4	29.6	68.7	85.4	84.2	53.8	52.2	81.6	69.8	65.0	40.0	7.6	40.0	35.0	17.6	22.1	18.9	$41.0	5.3	5.3	$2,170	7.9	1.8	265.6	35.6
Iraq	77	Iraq	Iraq	Middle East and North Africa	NaN	NaN	NaN	37.0	12.3	20.3	NaN	52.8	13.3	54.4	53.1	81.4	NaN	NaN	NaN	NaN	15.0	15.0	NaN	39.7	38.9	$658.8	-0.8	4.7	$16,954	8.2	0.1	-5,032.4	58.0
Mozambique	116	Mozambique	Mozambique	Sub-Saharan Africa	163.0	40.0	48.6	33.9	35.2	28.1	75.5	66.9	16.6	57.1	42.0	65.4	78.0	35.0	50.0	3.5	32.0	32.0	20.2	33.2	29.5	$36.7	3.0	5.6	$1,244	25.0	15.3	2,293.1	102.2
Belgium	15	Belgium	Belgium	Europe	48.0	25.0	67.3	81.3	61.6	72.5	47.1	15.2	73.4	78.1	61.0	76.1	86.0	85.0	70.0	2.0	50.0	29.0	44.2	53.2	11.4	$528.5	1.7	1.2	$46,553	7.4	2.2	740.4	103.2
Cuba	42	Cuba	Cuba	Americas	178.0	31.0	27.8	31.6	10.0	37.7	48.8	0.0	15.6	20.0	20.0	65.6	64.0	10.0	10.0	8.0	50.0	30.0	41.5	64.2	11.5	$148.0	0.9	1.9	$12,920	2.6	5.5	NaN	47.7
Canada	29	Canada	Canada	Americas	8.0	1.0	77.7	87.0	69.4	84.6	76.8	51.3	83.1	81.9	73.7	77.2	86.8	80.0	80.0	1.6	33.0	15.0	31.7	40.3	36.7	$1,769.3	3.0	2.1	$48,265	6.3	1.6	24,243.8	89.7
Bosnia and Herzegovina	20	Bosnia and Herzegovina	BosniaHerzegovina	Europe	83.0	37.0	61.9	40.2	37.9	30.2	84.3	46.1	96.6	49.7	67.0	83.1	82.6	65.0	60.0	1.2	10.0	10.0	37.0	42.4	3.5	$44.6	2.7	2.5	$12,724	25.6	1.3	425.2	41.0
Jamaica	81	Jamaica	Jamaica	Americas	39.0	5.0	68.6	60.7	49.2	45.0	80.2	76.0	80.0	78.0	73.6	82.6	68.4	80.0	50.0	10.8	25.0	25.0	27.1	28.3	2.8	$26.1	1.0	0.8	$9,163	12.5	4.4	888.0	104.1
Thailand	163	Thailand	Thailand	Asia-Pacific	43.0	10.0	68.3	53.7	45.9	36.4	81.3	85.8	96.5	82.5	63.9	75.2	83.0	55.0	60.0	3.5	35.0	20.0	15.6	21.8	69.1	$1,233.7	3.9	2.8	$17,856	1.1	0.7	7,635.2	41.9
Uruguay	176	Uruguay	Uruguay	Americas	40.0	6.0	68.6	68.3	58.9	69.2	77.2	67.5	69.9	74.3	71.9	72.9	78.6	85.0	30.0	5.7	30.0	25.0	27.4	32.9	3.5	$78.1	3.1	2.6	$22,371	7.9	6.2	-124.6	66.2
Macau	100	Macau	Macau	Asia-Pacific	34.0	9.0	71.0	60.0	60.0	33.2	77.1	90.4	100.0	60.0	50.0	76.5	90.0	85.0	70.0	0.0	12.0	39.0	25.0	17.9	0.6	$71.8	9.3	-0.6	$111,629	2.0	1.2	1,996.8	0.0
Taiwan	160	Taiwan	Taiwan	Asia-Pacific	10.0	5.0	77.3	85.4	70.1	69.2	75.0	90.6	91.6	93.2	60.9	84.4	87.0	60.0	60.0	1.5	45.0	20.0	8.9	17.7	23.6	$1,185.5	2.8	2.2	$50,294	3.8	0.6	3,255.0	35.2
Lao P.D.R.	91	Laos	Laos	Asia-Pacific	110.0	24.0	57.4	38.8	42.5	33.5	86.9	85.3	66.5	60.1	60.1	78.5	81.8	35.0	20.0	1.6	24.0	24.0	12.8	22.1	6.7	$49.2	6.8	7.4	$7,366	0.7	0.8	813.0	62.8
Eritrea	53	Eritrea	Eritrea	Sub-Saharan Africa	177.0	47.0	38.9	35.5	18.1	19.7	81.4	73.9	0.0	17.7	70.0	61.0	69.2	0.0	20.0	5.4	30.0	30.0	8.0	29.5	5.9	$9.4	5.0	3.4	$1,581	6.4	9.0	55.5	131.2
Venezuela	179	Venezuela	Venezuela	Americas	179.0	32.0	25.9	7.6	13.1	7.9	74.7	58.1	17.6	33.9	28.0	0.0	60.0	0.0	10.0	10.0	34.0	34.0	14.9	37.4	31.4	$380.7	-14.0	-7.8	$12,114	7.7	1087.5	-68.0	34.9
France	58	France	France	Europe	71.0	35.0	63.8	82.5	66.1	67.9	48.4	3.9	64.9	81.2	45.2	79.1	81.0	75.0	70.0	2.0	45.0	33.0	45.3	56.6	64.8	$2,835.8	1.8	1.1	$43,761	9.4	1.2	49,794.9	97.0
Hungary	72	Hungary	Hungary	Europe	64.0	31.0	65.0	60.9	45.2	35.3	78.6	31.7	85.0	61.1	64.7	81.8	86.0	80.0	70.0	2.0	15.0	19.0	39.4	47.7	9.8	$289.0	4.0	3.2	$29,474	4.2	2.4	2,491.6	69.9
Slovak Republic	148	Slovakia	Slovakia	Europe	65.0	32.0	65.0	68.5	37.2	37.7	78.6	46.1	87.2	61.3	53.4	78.6	86.0	75.0	70.0	2.0	25.0	21.0	32.7	42.4	5.4	$179.4	3.4	3.0	$33,025	8.1	1.3	2,276.7	50.4
Turkmenistan	170	Turkmenistan	Turkmenistan	Asia-Pacific	164.0	40.0	48.4	31.6	29.8	20.3	95.9	92.0	92.3	30.0	20.0	73.4	76.0	10.0	10.0	2.0	10.0	8.0	15.6	16.4	5.7	$103.5	6.5	7.9	$18,126	3.4	8.0	2,313.5	28.8
Namibia	117	Namibia	Namibia	Sub-Saharan Africa	99.0	10.0	58.7	55.9	54.7	49.8	66.5	48.9	15.7	65.8	85.1	74.4	83.0	65.0	40.0	1.0	37.0	34.0	28.6	41.3	2.3	$26.5	-1.2	3.6	$11,312	23.3	6.1	416.0	46.1
Bhutan	18	Bhutan	Bhutan	Asia-Pacific	74.0	16.0	62.9	62.5	55.4	54.5	83.0	71.6	77.6	68.7	79.5	72.6	79.4	20.0	30.0	2.8	25.0	30.0	13.4	30.7	0.8	$7.0	6.0	5.2	$8,744	2.4	3.4	10.3	102.4
Armenia	6	Armenia	Armenia	Europe	47.0	24.0	67.7	57.2	46.3	38.6	84.7	79.0	53.0	78.3	71.4	77.8	80.8	75.0	70.0	2.1	26.0	20.0	21.3	26.4	3.0	$28.3	7.5	3.6	$9,456	18.2	0.9	245.7	53.5
Mexico	110	Mexico	Mexico	Americas	66.0	12.0	64.7	59.1	34.9	26.3	75.8	78.2	83.2	67.8	58.6	75.9	81.4	75.0	60.0	4.3	35.0	30.0	17.2	26.9	123.5	$2,458.4	2.0	2.5	$19,903	3.4	6.0	29,695.0	54.2
Nicaragua	121	Nicaragua	Nicaragua	Americas	107.0	21.0	57.7	33.4	18.7	20.3	76.9	79.1	93.9	56.0	55.8	72.7	76.0	60.0	50.0	2.0	30.0	30.0	22.6	26.4	6.2	$36.4	4.9	4.8	$5,849	4.4	3.9	896.6	33.6
Kuwait	89	Kuwait	Kuwait	Middle East and North Africa	90.0	8.0	60.8	52.9	43.3	35.3	97.7	17.3	99.1	57.4	61.7	70.6	79.0	55.0	60.0	3.0	0.0	15.0	1.6	52.5	4.4	$291.5	-2.5	-0.1	$66,163	2.1	1.5	300.5	20.6
Finland	57	Finland	Finland	Europe	20.0	11.0	74.9	89.6	81.2	92.5	66.8	7.2	86.4	89.4	50.3	84.8	86.0	85.0	80.0	2.0	31.3	20.0	44.1	55.6	5.5	$244.0	3.0	0.8	$44,333	8.6	0.8	1,327.9	61.4
Latvia	92	Latvia	Latvia	Europe	35.0	18.0	70.4	67.3	48.4	35.5	77.0	57.1	96.9	77.5	73.3	81.1	86.0	85.0	60.0	2.0	31.4	20.0	30.2	37.8	2.0	$53.9	4.5	2.8	$27,644	8.7	2.9	721.2	34.8
Spain	152	Spain	Spain	Europe	57.0	28.0	65.7	72.9	51.4	51.9	62.3	46.2	51.1	66.8	57.8	87.5	86.0	85.0	70.0	2.0	45.0	25.0	33.5	42.3	46.3	$1,773.9	3.1	1.9	$38,286	17.2	2.0	19,086.1	98.4

	Country_Name	NA Count
Country
Cuba	Cuba	1
Dominica	Dominica	1
Iraq	Iraq	9
Kiribati	Kiribati	1
Korea, North	Korea, North	6
Kosovo	Kosovo	1
Libya	Libya	9
Liechtenstein	Liechtenstein	21
Micronesia	Micronesia	2
Seychelles	Seychelles	1
Somalia	Somalia	19
Syria	Syria	16
Yemen	Yemen	9

	World_Rank	Region_Rank	2019_Score	Property_Rights	Judical_Effectiveness	Government_Integrity	Tax_Burden	Government_Spending	Fiscal_Health	Business_Freedom	Labor_Freedom	Monetary_Freedom	Trade_Freedom	Investment_Freedom	Financial_Freedom	Tariff_Rate_Percent	Income_Tax_Rate_Percent	Corporate_Tax_Rate_Percent	Tax_Burden_Percent_of_GDP	Government_Expenditure_Percent_of_GDP	GDP_Growth_Rate_Percent	5_Year_GDP_Growth_Rate_Percent	Inflation_Percent	Public_Debt_by_Percent_of_GDP
count	173.000000	173.000000	173.000000	173.000000	173.000000	173.000000	173.000000	173.000000	173.000000	173.000000	173.000000	173.000000	173.000000	173.000000	173.000000	173.000000	173.000000	173.000000	173.000000	173.000000	173.000000	173.000000	173.000000	173.000000
mean	89.046243	20.219653	61.349133	53.561850	46.045087	42.284393	77.704046	66.019075	66.811561	64.722543	60.051445	75.682659	75.022543	58.583815	49.653179	5.623699	28.604046	23.916763	22.045665	32.023121	3.290751	3.171676	10.869942	56.667052
std	51.636567	12.623265	10.290731	19.229243	17.651070	19.925058	11.786874	20.957233	30.813333	14.770800	13.778284	9.501612	10.825901	21.408001	18.921378	4.408220	13.235103	8.878332	10.151558	10.387187	2.748959	2.467099	82.555305	33.854417
min	1.000000	1.000000	25.900000	7.600000	12.300000	7.900000	42.000000	0.900000	0.000000	17.700000	20.000000	0.000000	45.000000	0.000000	10.000000	0.000000	0.000000	0.000000	1.600000	10.600000	-14.000000	-7.800000	-0.900000	0.000000
25%	44.000000	9.000000	54.600000	37.400000	32.300000	28.100000	71.000000	54.100000	42.500000	55.800000	51.100000	72.400000	67.400000	45.000000	40.000000	2.000000	20.000000	20.000000	14.000000	23.600000	2.000000	2.000000	1.300000	35.200000
50%	90.000000	19.000000	60.800000	52.400000	44.000000	36.400000	78.600000	70.000000	80.300000	65.800000	60.100000	77.800000	76.600000	60.000000	50.000000	4.200000	30.000000	25.000000	20.700000	31.600000	3.200000	3.100000	2.800000	50.400000
75%	133.000000	30.000000	68.100000	66.000000	56.000000	50.500000	85.400000	83.300000	90.100000	75.200000	69.200000	81.800000	84.700000	75.000000	60.000000	8.700000	35.000000	30.000000	29.500000	39.100000	4.800000	4.500000	5.500000	70.200000
max	179.000000	47.000000	90.200000	97.400000	92.400000	96.700000	99.800000	96.600000	100.000000	96.400000	91.000000	88.000000	95.000000	95.000000	90.000000	18.600000	60.000000	50.000000	47.000000	57.500000	10.900000	9.900000	1087.500000	236.400000

	Country_Name	WEBNAME	Region	World_Rank	Region_Rank	2019_Score	Property_Rights	Judical_Effectiveness	Government_Integrity	Tax_Burden	Government_Spending	Fiscal_Health	Business_Freedom	Labor_Freedom	Monetary_Freedom	Trade_Freedom	Investment_Freedom	Financial_Freedom	Tariff_Rate_Percent	Income_Tax_Rate_Percent	Corporate_Tax_Rate_Percent	Tax_Burden_Percent_of_GDP	Government_Expenditure_Percent_of_GDP	Population_in_Millions	GDP_in_Billions_by_PPP_in_USA_Dollars	GDP_Growth_Rate_Percent	5_Year_GDP_Growth_Rate_Percent	GDP_per_Capita_by_PPP_in_USA_Dollars	Unemployment_Percent	Inflation_Percent	FDI_Inflow_in_Millions	Public_Debt_by_Percent_of_GDP
Country
Afghanistan	Afghanistan	Afghanistan	Asia-Pacific	152.0	39.0	51.5	19.6	29.6	25.2	91.7	80.3	99.3	49.2	60.4	76.7	66.0	10.0	10.0	7.0	20.0	20.0	5.0	25.6	35.5	$69.6	2.5	2.9	$1,958	8.8	5.0	53.9	7.3
Albania	Albania	Albania	Europe	52.0	27.0	66.5	54.8	30.6	40.4	86.3	73.9	80.6	69.3	52.7	81.5	87.8	70.0	70.0	1.1	23.0	15.0	24.9	29.5	2.9	$36.0	3.9	2.5	$12,507	13.9	2.0	1,119.1	71.2
Algeria	Algeria	Algeria	Middle East and North Africa	171.0	14.0	46.2	31.6	36.2	28.9	76.4	48.7	18.7	61.6	49.9	74.9	67.4	30.0	30.0	8.8	35.0	23.0	24.5	41.4	41.5	$632.9	2.0	3.1	$15,237	10.0	5.6	1,203.0	25.8
Angola	Angola	Angola	Sub-Saharan Africa	156.0	33.0	50.6	35.9	26.6	20.5	83.9	80.7	58.2	55.7	58.8	55.4	61.2	30.0	40.0	9.4	17.0	30.0	20.6	25.3	28.2	$190.3	0.7	2.9	$6,753	8.2	31.7	-2,254.5	65.3
Argentina	Argentina	Argentina	Americas	148.0	26.0	52.2	47.8	44.5	33.5	69.3	49.5	33.0	56.4	46.9	60.2	70.0	55.0	60.0	7.5	35.0	30.0	30.8	41.0	44.1	$920.2	2.9	0.7	$20,876	8.7	25.7	11,857.0	52.6
Armenia	Armenia	Armenia	Europe	47.0	24.0	67.7	57.2	46.3	38.6	84.7	79.0	53.0	78.3	71.4	77.8	80.8	75.0	70.0	2.1	26.0	20.0	21.3	26.4	3.0	$28.3	7.5	3.6	$9,456	18.2	0.9	245.7	53.5
Australia	Australia	Australia	Asia-Pacific	5.0	4.0	80.9	79.1	86.5	79.9	62.8	60.1	86.2	88.3	84.1	86.6	87.6	80.0	90.0	1.2	45.0	30.0	28.2	36.5	24.8	$1,246.5	2.3	2.4	$50,334	5.6	2.0	46,368.0	41.6
Austria	Austria	Austria	Europe	31.0	16.0	72.0	84.2	71.3	77.4	50.5	24.5	85.5	74.9	68.7	81.5	86.0	90.0	70.0	2.0	50.0	25.0	42.7	50.2	8.8	$439.6	2.9	1.3	$49,869	5.5	2.2	9,629.6	78.8
Azerbaijan	Azerbaijan	Azerbaijan	Asia-Pacific	60.0	13.0	65.4	59.1	53.1	44.7	87.5	59.5	89.4	69.5	63.9	63.0	74.6	60.0	60.0	5.2	25.0	20.0	15.0	36.7	9.8	$171.8	0.1	1.2	$17,492	5.0	13.0	2,867.0	54.7
Bahamas	Bahamas	Bahamas	Americas	76.0	15.0	62.9	42.2	46.9	43.7	97.3	86.8	65.7	68.5	67.5	78.1	47.8	50.0	60.0	18.6	0.0	0.0	16.3	21.0	0.4	$11.6	1.3	-0.7	$31,139	12.6	1.4	927.7	57.2
Bahrain	Bahrain	Bahrain	Middle East and North Africa	54.0	5.0	66.4	63.5	50.7	53.6	99.7	62.7	3.7	71.4	71.1	81.6	83.8	75.0	80.0	3.1	0.0	0.0	5.6	35.3	1.5	$70.4	3.2	3.8	$48,505	1.2	1.4	518.9	90.3
Bangladesh	Bangladesh	Bangladesh	Asia-Pacific	121.0	27.0	55.6	36.1	34.5	24.4	72.7	94.5	77.6	50.9	68.2	69.9	63.6	45.0	30.0	10.7	25.0	45.0	8.8	13.6	163.2	$687.1	7.1	6.7	$4,211	4.4	5.7	2,151.6	32.4
Barbados	Barbados	Barbados	Americas	67.0	13.0	64.7	52.9	59.9	53.8	70.1	65.0	79.5	69.8	59.9	78.3	56.6	70.0	60.0	14.2	35.0	25.0	33.7	34.1	0.3	$5.2	0.9	0.6	$18,664	9.7	4.4	286.2	132.9
Belarus	Belarus	Belarus	Europe	104.0	42.0	57.9	55.2	51.7	37.7	89.4	41.3	85.4	75.0	75.3	67.0	76.4	30.0	10.0	1.8	13.0	18.0	23.8	44.2	9.5	$178.9	2.4	-0.3	$18,931	0.5	6.0	1,276.4	51.0
Belgium	Belgium	Belgium	Europe	48.0	25.0	67.3	81.3	61.6	72.5	47.1	15.2	73.4	78.1	61.0	76.1	86.0	85.0	70.0	2.0	50.0	29.0	44.2	53.2	11.4	$528.5	1.7	1.2	$46,553	7.4	2.2	740.4	103.2
Belize	Belize	Belize	Americas	123.0	24.0	55.4	41.7	46.9	27.2	79.9	65.9	39.1	61.8	54.8	78.7	64.0	55.0	50.0	10.5	25.0	25.0	27.6	33.7	0.4	$3.2	0.8	1.8	$8,324	9.3	1.1	77.0	99.0
Benin	Benin	Benin	Sub-Saharan Africa	127.0	21.0	55.3	37.2	32.8	28.1	69.3	83.4	27.9	62.4	53.8	86.4	61.8	70.0	50.0	11.6	45.0	30.0	11.9	23.5	11.1	$25.3	5.6	5.0	$2,277	2.5	0.1	184.4	54.6
Bhutan	Bhutan	Bhutan	Asia-Pacific	74.0	16.0	62.9	62.5	55.4	54.5	83.0	71.6	77.6	68.7	79.5	72.6	79.4	20.0	30.0	2.8	25.0	30.0	13.4	30.7	0.8	$7.0	6.0	5.2	$8,744	2.4	3.4	10.3	102.4
Bolivia	Bolivia	Bolivia	Americas	173.0	30.0	42.3	20.5	12.3	19.7	82.4	49.3	17.6	58.8	52.9	68.8	70.4	15.0	40.0	4.8	13.0	25.0	31.1	41.1	11.1	$83.6	4.2	5.1	$7,547	3.1	2.8	724.7	50.9
Bosnia and Herzegovina	Bosnia and Herzegovina	BosniaHerzegovina	Europe	83.0	37.0	61.9	40.2	37.9	30.2	84.3	46.1	96.6	49.7	67.0	83.1	82.6	65.0	60.0	1.2	10.0	10.0	37.0	42.4	3.5	$44.6	2.7	2.5	$12,724	25.6	1.3	425.2	41.0
Botswana	Botswana	Botswana	Sub-Saharan Africa	36.0	3.0	69.5	58.1	45.7	52.4	82.7	65.9	94.6	68.7	68.2	78.8	83.8	65.0	70.0	0.6	25.0	22.0	24.9	33.7	2.2	$38.9	2.2	4.1	$17,828	17.4	3.3	400.6	15.6
Brazil	Brazil	Brazil	Americas	150.0	27.0	51.9	57.3	51.7	28.1	70.5	55.2	5.9	57.9	51.9	75.5	69.0	50.0	50.0	8.0	27.5	34.0	32.2	38.6	207.7	$3,240.3	1.0	-0.5	$15,603	13.3	3.4	62,712.6	84.0
Brunei Darussalam	Brunei Darussalam	Brunei	Asia-Pacific	63.0	14.0	65.1	64.0	56.0	43.7	90.7	59.9	20.0	80.2	90.8	76.5	84.0	65.0	50.0	0.5	0.0	18.5	24.2	36.6	0.4	$33.5	0.5	-1.4	$78,196	7.1	-0.1	-46.3	2.7
Bulgaria	Bulgaria	Bulgaria	Europe	37.0	19.0	69.0	62.5	41.9	35.1	90.2	63.9	98.8	62.7	68.4	88.0	86.0	70.0	60.0	2.0	10.0	10.0	28.0	34.7	7.1	$153.1	3.6	2.7	$21,687	6.2	1.2	1,070.7	23.9
Burkina Faso	Burkina Faso	BurkinaFaso	Sub-Saharan Africa	96.0	9.0	59.4	49.1	42.9	36.6	81.9	80.0	61.8	51.6	52.3	86.2	65.2	65.0	40.0	7.4	27.5	28.0	16.3	25.8	18.9	$35.8	6.4	5.3	$1,889	6.3	0.4	485.9	38.3
Burma	Burma	Burma	Asia-Pacific	139.0	35.0	53.6	34.7	18.1	30.6	86.6	85.4	78.3	52.8	65.7	69.6	70.8	30.0	20.0	4.6	20.0	30.0	6.5	22.1	52.6	$328.7	6.7	7.2	$6,244	0.8	5.1	4,341.0	34.7
Burundi	Burundi	Burundi	Sub-Saharan Africa	162.0	39.0	48.9	20.6	31.0	26.2	74.0	83.3	23.3	50.3	67.5	62.2	68.2	50.0	30.0	5.9	35.0	35.0	12.3	23.6	10.9	$8.0	0.0	1.1	$735	1.6	16.6	0.3	56.7
Cambodia	Cambodia	Cambodia	Asia-Pacific	105.0	22.0	57.8	37.4	27.6	16.7	89.7	85.9	89.1	29.9	63.0	79.4	65.4	60.0	50.0	9.8	20.0	20.0	15.0	21.7	16.0	$64.3	6.9	7.1	$4,012	0.2	2.9	2,784.4	35.1
Cameroon	Cameroon	Cameroon	Sub-Saharan Africa	145.0	29.0	52.4	42.5	31.3	25.5	74.4	87.5	58.4	44.4	47.8	84.0	53.4	30.0	50.0	15.8	35.0	33.0	15.6	20.4	24.3	$88.9	3.2	4.9	$3,660	4.2	0.6	672.5	33.8
Canada	Canada	Canada	Americas	8.0	1.0	77.7	87.0	69.4	84.6	76.8	51.3	83.1	81.9	73.7	77.2	86.8	80.0	80.0	1.6	33.0	15.0	31.7	40.3	36.7	$1,769.3	3.0	2.1	$48,265	6.3	1.6	24,243.8	89.7
Cabo Verde	Cabo Verde	CaboVerde	Sub-Saharan Africa	73.0	4.0	63.1	44.1	49.0	43.7	76.4	71.2	59.7	65.2	55.7	84.1	68.2	80.0	60.0	10.9	35.0	24.0	23.7	31.0	0.5	$3.7	4.0	2.0	$6,944	10.3	0.8	108.6	126.0
Central African Republic	Central African Republic	CentralAfricanRepublic	Sub-Saharan Africa	161.0	38.0	49.1	19.6	29.6	23.2	65.2	94.2	94.3	24.2	40.1	72.3	51.0	45.0	30.0	14.5	50.0	30.0	9.0	13.9	5.0	$3.4	4.0	-4.5	$677	6.0	3.8	17.2	53.4
Chad	Chad	Chad	Sub-Saharan Africa	159.0	36.0	49.9	26.7	24.6	23.2	46.1	92.4	85.2	28.1	43.2	82.3	47.2	60.0	40.0	16.4	60.0	45.0	5.3	15.9	12.2	$28.6	-3.1	1.0	$2,344	5.9	-0.9	335.0	52.5
Chile	Chile	Chile	Americas	18.0	3.0	75.4	68.7	56.3	62.3	77.3	81.0	89.0	76.6	65.0	84.5	88.8	85.0	70.0	0.6	35.0	25.0	20.4	25.2	18.4	$451.1	1.5	2.2	$24,537	7.0	2.2	6,729.6	23.6
China	China	China	Asia-Pacific	100.0	20.0	58.4	49.9	75.2	49.1	70.4	70.1	76.0	56.2	64.2	71.9	73.0	25.0	20.0	3.5	45.0	25.0	17.5	31.6	1390.1	$23,159.1	6.9	7.1	$16,660	4.7	1.6	136,320.0	47.8
Colombia	Colombia	Colombia	Americas	49.0	8.0	67.3	59.2	34.3	33.5	74.3	75.0	79.2	71.4	78.5	75.6	76.0	80.0	70.0	7.0	33.0	33.0	19.9	28.9	49.3	$714.0	1.8	3.2	$14,485	8.9	4.3	14,518.0	49.4
Comoros	Comoros	Comoros	Sub-Saharan Africa	124.0	20.0	55.4	36.5	29.6	24.4	63.9	73.4	91.7	57.2	60.3	82.8	70.0	45.0	30.0	5.0	30.0	50.0	14.5	29.8	0.8	$1.3	2.5	2.2	$1,588	4.3	1.0	8.6	28.4
Congo, Democratic Republic of the Congo	Congo, Democratic Republic of the Congo	DemcoraticRepublicCongo	Sub-Saharan Africa	157.0	34.0	50.3	25.3	30.7	26.2	73.8	93.9	96.9	53.2	41.9	49.1	62.6	30.0	20.0	11.2	30.0	40.0	10.8	14.2	86.7	$68.5	3.4	6.1	$790	3.7	41.5	1,340.2	15.7
Congo, Republic of	Congo, Republic of	RepublicCongo	Sub-Saharan Africa	176.0	46.0	39.7	33.2	29.6	25.3	59.5	40.6	0.0	38.2	35.8	82.6	56.8	45.0	30.0	11.6	45.0	34.0	29.5	44.5	4.3	$28.9	-4.6	1.1	$6,642	11.0	0.5	1,158.8	119.1
Costa Rica	Costa Rica	CostaRica	Americas	61.0	11.0	65.3	58.3	54.0	54.5	79.2	88.4	42.5	67.2	55.2	83.2	81.4	70.0	50.0	1.8	25.0	30.0	23.6	19.6	5.0	$83.9	3.2	3.4	$16,877	8.1	1.6	3,007.1	49.1
Côte d'Ivoire	Côte d'Ivoire	CoeDivoire	Sub-Saharan Africa	78.0	5.0	62.4	40.9	47.8	38.1	77.5	83.9	74.3	61.0	52.5	74.2	73.6	75.0	50.0	8.2	36.0	25.0	18.2	23.1	25.0	$96.9	7.8	8.6	$3,883	2.6	0.8	674.7	46.4
Croatia	Croatia	Croatia	Europe	86.0	38.0	61.4	66.0	42.9	38.6	66.4	33.4	85.4	60.7	44.0	78.5	86.0	75.0	60.0	2.0	40.0	18.0	37.9	47.1	4.1	$101.3	2.8	1.5	$24,424	11.2	1.1	2,104.2	78.4
Cyprus	Cyprus	Cyprus	Europe	44.0	22.0	68.1	73.1	48.1	43.7	74.9	55.2	80.3	76.9	59.5	84.0	86.0	75.0	60.0	2.0	35.0	12.5	33.6	38.7	0.9	$31.6	3.9	0.3	$37,023	11.0	0.7	6,343.3	99.3
Czech Republic	Czech Republic	CzechRepublic	Europe	23.0	13.0	73.7	74.8	47.6	52.1	82.6	52.1	97.6	72.4	78.1	81.5	86.0	80.0	80.0	2.0	15.0	19.0	34.0	40.0	10.6	$375.7	4.3	2.9	$35,512	2.9	2.4	7,412.2	34.7
Denmark	Denmark	Denmark	Europe	14.0	6.0	76.7	86.2	77.8	85.8	42.0	14.4	96.7	90.7	86.4	84.1	86.0	90.0	80.0	2.0	56.0	23.5	45.9	53.4	5.7	$286.8	2.1	1.6	$49,883	5.7	1.1	-3,114.7	36.4
Djibouti	Djibouti	Djibouti	Sub-Saharan Africa	169.0	43.0	47.1	29.7	18.1	28.1	76.2	27.3	18.1	54.7	60.4	72.7	50.4	80.0	50.0	17.3	30.0	25.0	29.2	49.2	1.0	$3.6	6.7	6.1	$3,559	5.8	0.7	165.0	30.6
Dominican Republic	Dominican Republic	DominicanRepublic	Americas	89.0	19.0	61.0	50.6	18.1	23.2	84.6	90.3	89.9	51.9	57.6	79.7	75.8	70.0	40.0	4.6	25.0	27.0	13.6	17.9	10.2	$172.4	4.6	6.1	$16,944	5.5	3.3	3,570.0	37.7
Ecuador	Ecuador	Ecuador	Americas	170.0	29.0	46.9	35.9	20.2	25.3	77.0	55.5	32.1	54.1	48.2	73.5	66.4	35.0	40.0	6.8	35.0	25.0	21.2	38.5	16.8	$192.6	2.7	2.0	$11,482	3.8	0.4	606.4	45.0
Egypt	Egypt	Egypt	Middle East and North Africa	144.0	11.0	52.5	37.0	48.3	29.2	85.2	68.1	0.0	65.9	51.6	62.3	71.8	60.0	50.0	6.6	25.0	23.0	18.0	32.6	94.8	$1,201.2	4.2	3.8	$12,671	12.1	23.5	7,391.7	103.3
El Salvador	El Salvador	ElSalvador	Americas	84.0	17.0	61.8	37.6	29.1	23.4	78.1	86.3	81.9	57.2	53.1	79.0	81.4	75.0	60.0	1.8	30.0	30.0	19.7	21.4	6.4	$57.0	2.4	2.1	$8,948	4.5	1.0	791.9	59.3
Equatorial Guinea	Equatorial Guinea	EquatorialGuinea	Sub-Saharan Africa	174.0	44.0	41.0	29.7	18.1	15.8	71.3	67.6	16.4	37.6	32.7	83.7	48.8	40.0	30.0	15.6	35.0	35.0	20.4	32.8	0.8	$30.4	-4.4	-5.6	$36,017	6.9	0.7	304.1	42.7
Eritrea	Eritrea	Eritrea	Sub-Saharan Africa	177.0	47.0	38.9	35.5	18.1	19.7	81.4	73.9	0.0	17.7	70.0	61.0	69.2	0.0	20.0	5.4	30.0	30.0	8.0	29.5	5.9	$9.4	5.0	3.4	$1,581	6.4	9.0	55.5	131.2
Estonia	Estonia	Estonia	Europe	15.0	7.0	76.6	81.5	76.0	73.1	79.9	51.1	99.8	75.3	57.2	79.6	86.0	90.0	70.0	2.0	20.0	20.0	34.7	40.4	1.3	$41.6	4.9	2.7	$31,750	5.8	3.7	784.4	8.8
Eswatini	Eswatini	Eswatini	Sub-Saharan Africa	132.0	23.0	54.7	41.7	42.9	35.0	74.8	65.6	18.3	59.2	67.5	73.7	87.6	50.0	40.0	1.2	33.0	28.0	25.5	33.9	1.1	$11.3	0.2	2.0	$9,884	26.4	6.3	-136.8	29.2
Ethiopia	Ethiopia	Ethiopia	Sub-Saharan Africa	137.0	26.0	53.6	32.6	40.9	35.1	77.2	90.4	83.3	48.6	58.0	60.8	60.8	35.0	20.0	12.1	35.0	30.0	12.4	17.9	92.7	$200.2	10.9	9.9	$2,161	5.2	9.9	3,586.4	56.2
Fiji	Fiji	Fiji	Asia-Pacific	81.0	18.0	62.2	67.3	42.9	23.4	81.1	71.7	82.4	63.0	72.9	73.5	62.8	55.0	50.0	11.1	29.0	20.0	25.5	30.7	0.9	$8.7	3.8	3.7	$9,777	6.3	3.4	299.0	46.6
Finland	Finland	Finland	Europe	20.0	11.0	74.9	89.6	81.2	92.5	66.8	7.2	86.4	89.4	50.3	84.8	86.0	85.0	80.0	2.0	31.3	20.0	44.1	55.6	5.5	$244.0	3.0	0.8	$44,333	8.6	0.8	1,327.9	61.4
France	France	France	Europe	71.0	35.0	63.8	82.5	66.1	67.9	48.4	3.9	64.9	81.2	45.2	79.1	81.0	75.0	70.0	2.0	45.0	33.0	45.3	56.6	64.8	$2,835.8	1.8	1.1	$43,761	9.4	1.2	49,794.9	97.0
Gabon	Gabon	Gabon	Sub-Saharan Africa	118.0	17.0	56.3	28.1	30.6	35.5	75.8	86.6	82.1	52.1	53.0	80.0	51.2	60.0	40.0	16.9	35.0	30.0	17.3	21.1	1.9	$36.7	0.8	3.4	$19,254	19.7	3.0	1,498.0	61.1
Gambia	Gambia	Gambia	Sub-Saharan Africa	146.0	30.0	52.4	39.9	42.5	41.2	74.3	70.7	0.0	54.0	67.4	62.4	61.6	65.0	50.0	14.2	35.0	32.0	17.9	31.3	2.1	$3.6	3.5	3.1	$1,713	9.5	8.0	87.5	123.2
Georgia	Georgia	Georgia	Europe	16.0	8.0	75.9	65.9	54.6	58.5	87.1	73.6	93.9	85.8	76.6	76.0	88.6	80.0	70.0	0.7	20.0	15.0	25.8	29.6	3.7	$39.7	4.8	3.7	$10,747	11.6	6.0	1,861.9	44.9
Germany	Germany	Germany	Europe	24.0	14.0	73.5	79.9	75.4	81.3	60.8	42.3	91.8	83.3	52.8	77.9	86.0	80.0	70.0	2.0	47.5	15.8	37.6	43.9	82.7	$4,170.8	2.5	1.7	$50,425	3.8	1.7	34,726.3	64.1
Ghana	Ghana	Ghana	Sub-Saharan Africa	109.0	13.0	57.5	49.1	44.2	35.5	78.8	82.0	23.9	56.5	59.9	66.3	63.4	70.0	60.0	10.8	35.0	25.0	16.5	24.5	28.3	$133.7	8.4	5.5	$4,729	2.4	12.4	3,255.0	71.8
Greece	Greece	Greece	Europe	106.0	43.0	57.7	52.4	49.5	37.7	59.1	23.3	79.0	74.1	52.5	79.1	81.0	55.0	50.0	2.0	42.0	29.0	38.6	50.6	10.8	$298.7	1.4	-0.3	$27,737	21.5	1.1	4,046.0	181.9
Guatemala	Guatemala	Guatemala	Americas	77.0	16.0	62.6	40.3	32.3	26.4	79.2	95.6	96.2	53.6	48.7	77.0	82.2	70.0	50.0	1.4	31.0	31.0	12.6	12.1	16.9	$137.8	2.8	3.6	$8,145	2.7	4.4	1,146.7	24.4
Guinea	Guinea	Guinea	Sub-Saharan Africa	120.0	19.0	55.7	34.7	32.6	25.5	69.4	89.8	87.2	54.6	54.9	66.4	63.2	50.0	40.0	10.9	40.0	35.0	15.3	18.4	13.0	$26.5	6.7	4.9	$2,041	4.5	8.9	576.5	39.7
Guinea-Bissau	Guinea-Bissau	GuineaBissau	Sub-Saharan Africa	135.0	25.0	54.0	32.6	42.9	25.3	88.8	86.7	81.4	35.9	61.2	78.1	55.6	30.0	30.0	12.2	20.0	25.0	9.6	21.0	1.7	$3.1	5.5	4.3	$1,845	6.1	1.1	16.6	42.0
Guyana	Guyana	Guyana	Americas	113.0	23.0	56.8	41.7	42.9	33.2	67.0	69.4	77.6	59.3	62.0	76.9	66.8	55.0	30.0	6.6	33.3	40.0	24.4	32.0	0.8	$6.3	2.1	3.5	$8,161	12.0	2.1	212.2	50.7
Haiti	Haiti	Haiti	Americas	143.0	25.0	52.7	10.4	25.3	20.3	79.9	88.3	95.9	36.2	62.6	66.5	72.0	45.0	30.0	6.5	30.0	30.0	14.5	19.7	11.0	$19.9	1.2	2.2	$1,815	14.0	14.7	374.9	31.1
Honduras	Honduras	Honduras	Americas	93.0	20.0	60.2	43.4	31.0	25.3	82.8	78.2	95.9	56.9	32.0	73.0	79.4	65.0	60.0	2.8	25.0	25.0	21.6	27.0	8.3	$46.2	4.8	3.6	$5,562	4.5	3.9	1,185.7	43.9
Hong Kong SAR	Hong Kong	HongKong	Asia-Pacific	1.0	1.0	90.2	93.3	75.3	83.8	93.1	90.3	100.0	96.4	89.2	86.4	95.0	90.0	90.0	0.0	15.0	16.5	14.0	17.9	7.4	$454.9	3.8	2.8	$61,393	3.1	1.5	104,333.0	0.1
Hungary	Hungary	Hungary	Europe	64.0	31.0	65.0	60.9	45.2	35.3	78.6	31.7	85.0	61.1	64.7	81.8	86.0	80.0	70.0	2.0	15.0	19.0	39.4	47.7	9.8	$289.0	4.0	3.2	$29,474	4.2	2.4	2,491.6	69.9
Iceland	Iceland	Iceland	Europe	11.0	4.0	77.1	87.4	63.8	83.8	72.7	44.0	96.7	88.4	64.1	81.7	87.0	85.0	70.0	1.5	31.8	20.0	36.4	43.2	0.3	$17.6	3.6	4.4	$51,842	2.8	1.8	-5.4	40.9
India	India	India	Asia-Pacific	129.0	31.0	55.2	57.3	61.6	47.8	79.4	77.3	14.7	57.1	41.8	72.4	72.4	40.0	40.0	6.3	30.9	32.4	7.2	27.5	1316.9	$9,459.0	6.7	7.2	$7,183	3.5	3.6	39,916.1	70.2
Indonesia	Indonesia	Indonesia	Asia-Pacific	56.0	11.0	65.8	52.2	53.5	39.5	83.7	91.4	88.1	69.3	49.3	77.4	79.8	45.0	60.0	2.6	30.0	25.0	10.4	16.9	262.0	$3,242.8	5.1	5.1	$12,377	4.2	3.8	23,063.1	28.9
Iran	Iran	Iran	Middle East and North Africa	155.0	13.0	51.1	33.5	41.3	35.0	80.9	89.8	89.5	62.2	50.7	60.1	54.6	5.0	10.0	15.2	35.0	25.0	8.0	18.5	81.4	$1,644.7	4.3	3.6	$20,200	12.5	9.9	5,019.0	40.9
Ireland	Ireland	Ireland	Europe	6.0	2.0	80.5	85.8	68.4	78.0	76.3	77.4	89.0	83.1	75.3	87.0	86.0	90.0	70.0	2.0	41.0	12.5	23.0	27.4	4.7	$357.2	7.8	9.7	$75,538	6.4	0.3	28,974.6	68.5
Israel	Israel	Israel	Middle East and North Africa	27.0	2.0	72.8	80.0	73.4	67.9	61.9	52.4	85.3	71.4	65.1	86.2	84.4	75.0	70.0	2.8	48.0	23.0	31.2	39.8	8.7	$316.5	3.3	3.5	$36,340	4.3	0.2	18,954.0	61.0
Italy	Italy	Italy	Europe	80.0	36.0	62.2	71.7	49.8	43.7	55.6	26.5	71.3	71.7	51.1	84.0	86.0	85.0	50.0	2.0	43.0	27.5	42.9	49.5	60.6	$2,310.9	1.5	0.3	$38,140	11.2	1.3	17,077.1	131.5
Jamaica	Jamaica	Jamaica	Americas	39.0	5.0	68.6	60.7	49.2	45.0	80.2	76.0	80.0	78.0	73.6	82.6	68.4	80.0	50.0	10.8	25.0	25.0	27.1	28.3	2.8	$26.1	1.0	0.8	$9,163	12.5	4.4	888.0	104.1
Japan	Japan	Japan	Asia-Pacific	30.0	8.0	72.1	84.1	68.5	78.0	68.2	55.0	55.7	80.5	79.0	85.9	80.0	70.0	60.0	2.5	40.8	23.9	30.7	38.7	126.7	$5,428.8	1.7	1.3	$42,832	2.8	0.5	10,429.8	236.4
Jordan	Jordan	Jordan	Middle East and North Africa	53.0	4.0	66.5	58.4	52.6	50.3	91.4	73.4	60.6	61.8	52.7	85.0	81.4	70.0	60.0	4.3	14.0	20.0	16.3	29.8	7.1	$89.1	2.3	2.5	$12,494	14.9	3.3	1,664.8	95.6
Kazakhstan	Kazakhstan	Kazakhstan	Asia-Pacific	59.0	12.0	65.4	59.3	56.1	40.3	93.4	83.7	41.0	73.9	86.2	70.9	80.0	50.0	50.0	2.5	10.0	20.0	12.8	23.3	18.2	$477.6	4.0	3.3	$26,252	4.9	7.4	4,633.7	21.2
Kenya	Kenya	Kenya	Sub-Saharan Africa	130.0	22.0	55.1	53.8	46.9	32.1	79.5	77.8	13.8	55.8	63.4	72.7	60.4	55.0	50.0	12.3	30.0	30.0	15.7	27.2	46.7	$163.1	4.8	5.5	$3,491	11.5	8.0	671.7	55.6
Korea, South	Korea, South	SouthKorea	Asia-Pacific	29.0	7.0	72.3	79.3	57.5	50.5	64.2	68.6	96.8	91.3	57.4	82.0	80.4	70.0	70.0	4.8	46.2	27.5	26.3	32.4	51.5	$2,029.0	3.1	3.0	$39,434	3.7	1.9	17,052.8	39.8
Kuwait	Kuwait	Kuwait	Middle East and North Africa	90.0	8.0	60.8	52.9	43.3	35.3	97.7	17.3	99.1	57.4	61.7	70.6	79.0	55.0	60.0	3.0	0.0	15.0	1.6	52.5	4.4	$291.5	-2.5	-0.1	$66,163	2.1	1.5	300.5	20.6
Kyrgyz Republic	Kyrgyz Republic	KyrgyzRepublic	Asia-Pacific	79.0	17.0	62.3	49.9	27.9	27.2	94.1	54.2	78.4	73.4	79.8	74.4	78.6	60.0	50.0	3.2	10.0	10.0	19.7	39.1	6.3	$23.0	4.5	5.4	$3,667	7.3	3.2	93.8	59.1
Lao P.D.R.	Laos	Laos	Asia-Pacific	110.0	24.0	57.4	38.8	42.5	33.5	86.9	85.3	66.5	60.1	60.1	78.5	81.8	35.0	20.0	1.6	24.0	24.0	12.8	22.1	6.7	$49.2	6.8	7.4	$7,366	0.7	0.8	813.0	62.8
Latvia	Latvia	Latvia	Europe	35.0	18.0	70.4	67.3	48.4	35.5	77.0	57.1	96.9	77.5	73.3	81.1	86.0	85.0	60.0	2.0	31.4	20.0	30.2	37.8	2.0	$53.9	4.5	2.8	$27,644	8.7	2.9	721.2	34.8
Lebanon	Lebanon	Lebanon	Middle East and North Africa	154.0	12.0	51.1	39.5	26.6	18.2	91.8	75.6	0.0	47.9	46.5	78.1	79.0	60.0	50.0	3.0	20.0	15.0	13.8	28.5	4.5	$87.7	1.2	1.5	$19,439	6.6	4.5	2,628.0	152.8
Lesotho	Lesotho	Lesotho	Sub-Saharan Africa	142.0	28.0	53.1	41.5	45.7	30.9	59.4	33.0	63.5	53.3	58.8	75.0	81.0	55.0	40.0	2.0	35.0	25.0	47.0	47.3	1.9	$7.0	3.1	2.8	$3,581	27.3	5.6	135.0	34.7
Liberia	Liberia	Liberia	Sub-Saharan Africa	160.0	37.0	49.7	26.7	39.0	24.2	82.7	62.1	69.1	50.6	38.3	68.9	60.1	55.0	20.0	12.4	25.0	25.0	21.8	35.5	4.5	$6.1	2.5	2.1	$1,354	2.4	12.4	247.8	34.4
Lithuania	Lithuania	Lithuania	Europe	21.0	12.0	74.2	73.6	61.2	47.8	86.4	65.1	97.3	75.2	63.6	84.6	86.0	80.0	70.0	2.0	15.0	15.0	30.2	34.1	2.8	$91.2	3.8	3.0	$32,299	7.1	3.7	595.4	36.5
Luxembourg	Luxembourg	Luxembourg	Europe	17.0	9.0	75.9	83.0	72.4	85.8	65.4	46.6	98.9	68.8	45.9	82.6	86.0	95.0	80.0	2.0	42.0	18.0	37.1	42.2	0.6	$62.7	3.5	3.8	$106,374	5.5	2.1	6,622.7	23.0
Macau	Macau	Macau	Asia-Pacific	34.0	9.0	71.0	60.0	60.0	33.2	77.1	90.4	100.0	60.0	50.0	76.5	90.0	85.0	70.0	0.0	12.0	39.0	25.0	17.9	0.6	$71.8	9.3	-0.6	$111,629	2.0	1.2	1,996.8	0.0
Macedonia	Macedonia	Macedonia	Europe	33.0	17.0	71.1	65.1	60.7	44.7	91.8	70.0	82.9	80.2	71.5	78.7	82.0	65.0	60.0	4.0	10.0	10.0	24.8	31.6	2.1	$31.0	0.0	2.7	$14,914	22.4	1.4	256.3	39.3
Madagascar	Madagascar	Madagascar	Sub-Saharan Africa	114.0	15.0	56.6	33.2	24.4	14.3	91.0	91.8	85.5	47.3	44.6	72.4	69.2	55.0	50.0	7.9	20.0	20.0	10.1	16.6	25.6	$39.7	4.1	3.4	$1,551	1.8	8.1	389.1	37.3
Malawi	Malawi	Malawi	Sub-Saharan Africa	153.0	32.0	51.4	35.8	40.1	25.2	79.8	70.3	19.1	41.7	64.0	65.5	75.4	50.0	50.0	4.8	30.0	30.0	14.8	31.5	19.2	$22.4	4.0	4.0	$1,167	5.9	11.5	277.1	59.3
Malaysia	Malaysia	Malaysia	Asia-Pacific	22.0	6.0	74.0	84.1	68.2	55.4	85.6	83.2	82.4	83.9	74.4	78.6	82.0	60.0	50.0	4.0	25.0	25.0	13.8	23.7	32.1	$930.8	5.9	5.2	$29,041	3.4	3.8	9,543.4	54.2
Maldives	Maldives	Maldives	Asia-Pacific	141.0	37.0	53.2	43.9	36.4	33.5	95.8	60.8	10.7	78.3	70.8	81.0	62.6	35.0	30.0	11.2	0.0	0.0	20.5	36.1	0.4	$6.9	4.8	5.2	$19,151	5.0	2.8	517.5	68.1
Mali	Mali	Mali	Sub-Saharan Africa	103.0	12.0	58.1	33.7	33.4	29.6	68.7	85.4	84.2	53.8	52.2	81.6	69.8	65.0	40.0	7.6	40.0	35.0	17.6	22.1	18.9	$41.0	5.3	5.3	$2,170	7.9	1.8	265.6	35.6
Malta	Malta	Malta	Europe	41.0	20.0	68.6	69.8	50.4	50.3	64.2	56.1	94.5	67.1	61.3	78.2	86.0	85.0	60.0	2.0	35.0	35.0	33.6	38.3	0.5	$19.3	6.6	6.9	$41,945	4.0	1.3	3,185.2	52.6
Mauritania	Mauritania	Mauritania	Sub-Saharan Africa	119.0	18.0	55.7	27.5	30.6	30.6	78.0	74.2	80.6	61.9	51.5	81.2	62.6	50.0	40.0	8.7	30.0	25.0	25.9	29.3	3.9	$17.3	3.2	3.5	$4,444	9.9	2.3	329.6	91.1
Mauritius	Mauritius	Mauritius	Sub-Saharan Africa	25.0	1.0	73.0	69.5	62.1	40.3	92.1	80.3	73.6	79.8	60.8	79.4	88.4	80.0	70.0	0.8	15.0	15.0	18.4	25.6	1.3	$27.5	3.9	3.6	$21,640	7.1	3.7	292.7	60.2
Mexico	Mexico	Mexico	Americas	66.0	12.0	64.7	59.1	34.9	26.3	75.8	78.2	83.2	67.8	58.6	75.9	81.4	75.0	60.0	4.3	35.0	30.0	17.2	26.9	123.5	$2,458.4	2.0	2.5	$19,903	3.4	6.0	29,695.0	54.2
Moldova	Moldova	Moldova	Europe	97.0	40.0	59.1	55.2	29.6	25.4	85.4	59.1	92.0	67.0	39.0	73.5	78.0	55.0	50.0	3.5	18.0	12.0	31.5	36.9	3.5	$20.1	4.0	4.4	$5,661	4.5	6.6	213.8	37.7
Mongolia	Mongolia	Mongolia	Asia-Pacific	126.0	29.0	55.4	48.2	23.8	29.8	88.5	63.1	6.2	66.0	75.0	77.8	75.8	50.0	60.0	4.6	10.0	25.0	20.7	35.1	3.1	$39.7	5.1	5.7	$12,979	7.0	4.6	1,494.4	91.4
Montenegro	Montenegro	Montenegro	Europe	92.0	39.0	60.5	55.4	51.8	39.5	85.3	32.6	23.2	73.3	73.4	81.6	84.7	75.0	50.0	2.6	9.0	9.0	36.1	47.4	0.6	$11.0	4.2	3.2	$17,736	16.1	2.4	545.9	67.5
Morocco	Morocco	Morocco	Middle East and North Africa	75.0	6.0	62.9	57.2	47.1	39.2	72.2	72.7	66.9	70.3	33.1	83.5	77.4	65.0	70.0	3.8	38.0	30.0	20.9	30.2	34.9	$298.6	4.2	3.4	$8,567	9.3	0.8	2,651.4	64.4
Mozambique	Mozambique	Mozambique	Sub-Saharan Africa	163.0	40.0	48.6	33.9	35.2	28.1	75.5	66.9	16.6	57.1	42.0	65.4	78.0	35.0	50.0	3.5	32.0	32.0	20.2	33.2	29.5	$36.7	3.0	5.6	$1,244	25.0	15.3	2,293.1	102.2
Namibia	Namibia	Namibia	Sub-Saharan Africa	99.0	10.0	58.7	55.9	54.7	49.8	66.5	48.9	15.7	65.8	85.1	74.4	83.0	65.0	40.0	1.0	37.0	34.0	28.6	41.3	2.3	$26.5	-1.2	3.6	$11,312	23.3	6.1	416.0	46.1
Nepal	Nepal	Nepal	Asia-Pacific	136.0	34.0	53.8	39.2	34.7	26.2	84.0	83.7	98.5	61.8	47.9	69.4	60.4	10.0	30.0	12.3	25.0	25.0	18.7	23.3	29.3	$78.6	7.5	4.3	$2,679	2.7	4.5	198.0	27.2
Netherlands	Netherlands	Netherlands	Europe	13.0	5.0	76.8	88.0	74.7	89.1	51.6	42.9	93.3	81.4	60.3	84.0	86.0	90.0	80.0	2.0	52.0	25.0	38.8	43.6	17.1	$916.1	3.1	1.8	$53,635	4.8	1.3	57,956.7	56.7
New Zealand	New Zealand	NewZealand	Asia-Pacific	3.0	3.0	84.4	95.0	83.5	96.7	71.0	50.4	98.6	91.0	86.7	87.5	92.4	80.0	80.0	1.3	33.0	28.0	32.1	40.7	4.8	$188.6	3.0	3.3	$38,934	4.9	1.9	3,572.0	26.4
Nicaragua	Nicaragua	Nicaragua	Americas	107.0	21.0	57.7	33.4	18.7	20.3	76.9	79.1	93.9	56.0	55.8	72.7	76.0	60.0	50.0	2.0	30.0	30.0	22.6	26.4	6.2	$36.4	4.9	4.8	$5,849	4.4	3.9	896.6	33.6
Niger	Niger	Niger	Sub-Saharan Africa	151.0	31.0	51.6	37.2	31.0	34.1	76.9	75.6	22.2	56.3	48.2	76.7	65.8	55.0	40.0	9.6	35.0	30.0	13.5	28.5	18.8	$21.8	5.2	5.4	$1,164	0.4	2.4	334.3	46.5
Nigeria	Nigeria	Nigeria	Sub-Saharan Africa	111.0	14.0	57.3	36.5	34.3	20.5	85.0	96.5	68.2	51.2	83.3	65.0	62.4	45.0	40.0	11.3	24.0	30.0	5.2	10.8	188.7	$1,118.8	0.8	2.7	$5,929	7.0	16.5	3,503.0	23.4
Norway	Norway	Norway	Europe	26.0	15.0	73.0	86.1	81.2	92.3	57.4	25.3	97.3	89.4	53.7	75.4	83.2	75.0	60.0	3.4	47.8	23.0	38.0	49.9	5.3	$380.0	1.8	1.6	$71,831	4.2	1.9	-8,296.9	36.7
Oman	Oman	Oman	Middle East and North Africa	88.0	7.0	61.0	58.1	51.6	53.8	97.8	32.5	16.1	75.2	57.3	77.7	87.0	65.0	60.0	1.5	0.0	12.0	8.5	47.4	4.1	$186.6	-0.3	2.7	$45,157	3.3	1.6	1,867.4	44.2
Pakistan	Pakistan	Pakistan	Asia-Pacific	131.0	32.0	55.0	41.5	40.2	30.6	80.5	87.6	49.2	56.1	41.8	72.6	64.8	55.0	40.0	10.1	30.0	30.0	12.4	20.3	197.3	$1,057.0	5.3	4.3	$5,358	4.0	4.1	2,806.0	67.2
Panama	Panama	Panama	Americas	50.0	9.0	67.2	60.4	30.1	34.1	85.0	85.3	91.3	73.6	43.4	79.4	79.2	75.0	70.0	5.4	25.0	25.0	15.8	22.2	4.1	$103.9	5.4	5.8	$25,351	4.5	0.9	5,319.2	38.2
Papua New Guinea	Papua New Guinea	PapuaNewGuinea	Asia-Pacific	101.0	21.0	58.4	37.4	49.0	37.2	71.8	89.1	75.2	62.2	72.6	70.0	80.9	25.0	30.0	2.0	42.0	30.0	12.5	19.1	8.3	$30.3	2.5	5.8	$3,675	2.7	5.2	-200.5	32.6
Paraguay	Paraguay	Paraguay	Americas	85.0	18.0	61.8	39.5	30.0	25.5	96.3	78.9	96.3	61.5	29.2	72.8	76.6	75.0	60.0	4.2	10.0	10.0	13.0	26.5	7.0	$68.3	4.3	6.0	$9,826	4.6	3.6	355.8	25.6
Peru	Peru	Peru	Americas	45.0	7.0	67.8	56.1	34.0	31.8	80.6	86.1	88.5	67.8	63.5	83.9	86.4	75.0	60.0	1.8	30.0	28.0	16.0	21.5	31.8	$424.4	2.5	3.6	$13,334	3.6	2.8	6,769.5	25.5
Philippines	Philippines	Philippines	Asia-Pacific	70.0	15.0	63.8	48.7	36.4	30.9	76.9	88.7	97.1	61.3	57.9	69.6	78.2	60.0	60.0	3.4	35.0	30.0	13.7	19.4	105.3	$875.6	6.7	6.6	$8,315	2.4	3.2	9,524.3	37.8
Poland	Poland	Poland	Europe	46.0	23.0	67.8	62.3	44.0	49.8	74.9	48.8	86.4	65.4	63.9	82.1	86.0	80.0	70.0	2.0	32.0	19.0	33.6	41.3	38.0	$1,121.0	4.6	3.2	$29,521	4.9	2.0	6,433.5	51.4
Portugal	Portugal	Portugal	Europe	62.0	30.0	65.3	71.5	64.3	59.5	59.9	35.6	69.8	79.7	44.3	83.0	86.0	70.0	60.0	2.0	48.0	23.0	34.4	46.3	10.3	$313.4	2.7	1.2	$30,417	8.9	1.6	6,945.6	125.6
Qatar	Qatar	Qatar	Middle East and North Africa	28.0	3.0	72.6	64.5	60.0	77.4	99.7	56.8	94.0	71.2	65.9	78.4	83.2	60.0	60.0	3.4	0.0	0.0	5.7	37.9	2.7	$340.6	2.1	3.3	$124,529	0.1	0.4	986.0	54.0
Romania	Romania	Romania	Europe	42.0	21.0	68.6	66.7	51.9	39.8	89.7	69.0	89.3	63.1	64.5	82.7	86.0	70.0	50.0	2.0	10.0	16.0	26.0	32.1	19.6	$481.5	7.0	4.5	$24,508	4.9	1.3	5,160.0	36.9
Russia	Russia	Russia	Europe	98.0	41.0	58.9	52.4	45.1	36.6	89.4	62.3	86.6	78.4	52.5	65.1	77.8	30.0	30.0	3.6	13.0	20.0	22.2	35.4	144.0	$4,007.8	1.5	0.3	$27,834	5.2	3.7	25,284.0	17.4
Rwanda	Rwanda	Rwanda	Sub-Saharan Africa	32.0	2.0	71.1	72.2	83.2	67.9	79.8	79.4	86.3	56.2	82.2	76.1	70.4	60.0	40.0	7.3	30.0	30.0	15.0	26.2	11.8	$24.6	6.1	6.7	$2,080	1.3	4.8	366.2	40.6
Saint Lucia	Saint Lucia	SaintLucia	Americas	38.0	4.0	68.7	65.9	63.8	50.3	76.2	79.3	81.3	76.3	69.2	83.9	73.2	65.0	40.0	5.9	30.0	30.0	24.0	26.3	0.2	$2.5	3.0	1.9	$14,450	21.0	0.1	92.4	71.3
Saint Vincent and the Grenadines	Saint Vincent and the Grenadines	SaintVincentGrenadines	Americas	55.0	10.0	65.8	36.5	63.8	50.5	71.2	74.3	85.0	76.5	73.5	82.2	66.6	70.0	40.0	9.2	32.5	33.0	27.1	29.3	0.1	$1.3	1.0	1.1	$11,491	18.3	2.0	87.1	80.8
Samoa	Samoa	Samoa	Asia-Pacific	82.0	19.0	62.2	53.8	31.0	37.7	79.9	62.3	93.6	77.0	78.2	83.5	63.8	55.0	30.0	10.6	27.0	27.0	23.6	35.5	0.2	$1.1	2.4	2.1	$5,740	8.2	1.3	9.0	49.1
São Tomé and Príncipe	São Tomé and Príncipe	SaoTomePrincipe	Sub-Saharan Africa	134.0	24.0	54.0	37.4	26.6	35.5	87.2	67.0	62.3	65.1	42.7	70.5	64.2	60.0	30.0	10.4	20.0	25.0	15.9	33.2	0.2	$0.7	4.0	4.1	$3,180	13.5	5.5	41.0	83.3
Saudi Arabia	Saudi Arabia	SaudiArabia	Middle East and North Africa	91.0	9.0	60.7	55.0	62.7	49.8	99.8	57.5	19.4	72.3	63.3	78.1	76.0	45.0	50.0	4.5	2.5	2.5	3.4	37.6	32.4	$1,773.6	-0.7	2.3	$54,777	5.5	-0.9	1,421.0	17.3
Senegal	Senegal	Senegal	Sub-Saharan Africa	117.0	16.0	56.3	47.8	40.4	40.3	70.8	73.3	60.0	53.3	39.4	78.2	72.0	60.0	40.0	9.0	40.0	30.0	20.4	29.8	15.9	$43.2	7.2	5.6	$2,727	4.9	1.4	532.3	61.2
Serbia	Serbia	Serbia	Europe	69.0	34.0	63.9	50.1	44.8	37.2	82.0	45.1	90.1	72.9	67.4	80.0	77.0	70.0	50.0	6.5	10.0	15.0	38.4	42.8	7.0	$105.5	1.8	1.2	$15,000	14.1	3.1	2,866.7	61.5
Sierra Leone	Sierra Leone	SierraLeone	Sub-Saharan Africa	167.0	42.0	47.5	35.5	34.5	26.2	87.3	84.4	13.2	44.9	29.3	65.0	69.4	60.0	20.0	10.3	15.0	30.0	12.2	22.8	7.4	$11.5	3.5	2.9	$1,553	4.5	18.0	560.0	58.4
Singapore	Singapore	Singapore	Asia-Pacific	2.0	2.0	89.4	97.4	92.4	95.1	90.4	90.7	80.0	90.8	91.0	85.3	94.8	85.0	80.0	0.1	22.0	17.0	13.7	17.6	5.6	$527.0	3.6	3.5	$93,906	2.0	0.6	62,006.0	110.9
Slovak Republic	Slovakia	Slovakia	Europe	65.0	32.0	65.0	68.5	37.2	37.7	78.6	46.1	87.2	61.3	53.4	78.6	86.0	75.0	70.0	2.0	25.0	21.0	32.7	42.4	5.4	$179.4	3.4	3.0	$33,025	8.1	1.3	2,276.7	50.4
Slovenia	Slovenia	Slovenia	Europe	58.0	29.0	65.5	76.4	46.5	53.6	58.4	38.3	82.6	79.3	61.2	83.6	86.0	70.0	50.0	2.0	50.0	17.0	37.0	45.4	2.1	$71.1	5.0	2.5	$34,407	6.6	1.4	702.0	75.4
Solomon Islands	Solomon Islands	SolomonIslands	Asia-Pacific	133.0	33.0	54.6	49.9	51.7	33.5	65.5	36.5	89.4	68.6	72.0	86.0	56.8	15.0	30.0	14.1	40.0	30.0	30.8	46.0	0.6	$1.3	3.2	2.9	$2,157	2.1	-0.4	36.5	10.0
South Africa	South Africa	SouthAfrica	Sub-Saharan Africa	102.0	11.0	58.3	58.8	39.3	39.7	62.1	67.6	62.6	64.3	59.1	75.2	76.0	45.0	50.0	4.5	45.0	28.0	31.3	32.8	56.5	$765.6	1.3	1.5	$13,545	27.3	5.3	1,324.7	52.7
Spain	Spain	Spain	Europe	57.0	28.0	65.7	72.9	51.4	51.9	62.3	46.2	51.1	66.8	57.8	87.5	86.0	85.0	70.0	2.0	45.0	25.0	33.5	42.3	46.3	$1,773.9	3.1	1.9	$38,286	17.2	2.0	19,086.1	98.4
Sri Lanka	Sri Lanka	SriLanka	Asia-Pacific	115.0	25.0	56.4	44.7	39.4	28.9	84.9	88.3	30.4	75.1	58.8	70.1	76.2	40.0	40.0	4.4	24.0	28.0	12.3	19.8	21.4	$274.7	3.1	4.2	$12,811	4.1	6.5	1,374.9	79.4
Sudan	Sudan	Sudan	Sub-Saharan Africa	166.0	41.0	47.7	27.5	22.2	26.2	86.3	96.6	76.1	52.1	59.0	56.9	45.0	5.0	20.0	17.5	10.0	35.0	6.7	10.6	40.8	$187.0	3.2	3.0	$4,586	12.7	32.4	1,065.3	126.0
Suriname	Suriname	Suriname	Americas	165.0	28.0	48.1	49.1	22.2	35.5	70.9	77.2	9.6	48.3	73.5	56.0	64.6	40.0	30.0	10.2	38.0	36.0	13.2	27.6	0.6	$8.5	0.0	-0.9	$14,606	8.1	22.0	-87.3	72.1
Sweden	Sweden	Sweden	Europe	19.0	10.0	75.2	89.5	84.0	88.0	43.2	26.7	96.6	88.0	53.9	82.0	86.0	85.0	80.0	2.0	57.0	22.0	44.1	49.4	10.1	$520.9	2.4	2.8	$51,475	6.7	1.9	15,395.7	40.9
Switzerland	Switzerland	Switzerland	Europe	4.0	1.0	81.9	85.3	82.0	88.0	70.5	64.8	96.3	75.4	72.5	85.2	87.4	85.0	90.0	1.3	40.0	24.0	27.8	34.3	8.4	$517.2	1.1	1.6	$61,422	4.8	0.5	40,986.1	42.8
Taiwan	Taiwan	Taiwan	Asia-Pacific	10.0	5.0	77.3	85.4	70.1	69.2	75.0	90.6	91.6	93.2	60.9	84.4	87.0	60.0	60.0	1.5	45.0	20.0	8.9	17.7	23.6	$1,185.5	2.8	2.2	$50,294	3.8	0.6	3,255.0	35.2
Tajikistan	Tajikistan	Tajikistan	Asia-Pacific	122.0	28.0	55.6	47.8	52.1	36.4	91.8	64.6	60.3	67.3	49.2	68.5	73.6	25.0	30.0	5.7	13.0	15.0	20.6	34.3	8.8	$28.4	7.1	6.8	$3,212	10.3	7.3	141.3	47.8
Tanzania	Tanzania	Tanzania	Sub-Saharan Africa	94.0	7.0	60.2	35.4	41.4	33.2	80.5	90.3	85.2	46.6	66.2	70.4	67.8	55.0	50.0	8.6	30.0	30.0	12.4	18.0	50.0	$162.2	6.0	6.8	$3,240	2.2	5.3	1,180.4	38.2
Thailand	Thailand	Thailand	Asia-Pacific	43.0	10.0	68.3	53.7	45.9	36.4	81.3	85.8	96.5	82.5	63.9	75.2	83.0	55.0	60.0	3.5	35.0	20.0	15.6	21.8	69.1	$1,233.7	3.9	2.8	$17,856	1.1	0.7	7,635.2	41.9
Timor-Leste	Timor-Leste	TimorLeste	Asia-Pacific	172.0	42.0	44.2	29.7	13.1	32.1	96.3	0.9	20.0	60.5	58.8	79.5	75.0	45.0	20.0	2.5	10.0	10.0	13.1	57.5	1.2	$6.8	-0.5	3.1	$5,444	3.4	0.6	6.7	0.0
Togo	Togo	Togo	Sub-Saharan Africa	158.0	35.0	50.3	35.5	29.6	28.1	67.8	77.0	24.5	50.4	46.7	79.1	69.4	65.0	30.0	10.3	45.0	27.0	21.5	27.7	7.8	$12.9	4.4	5.5	$1,659	1.8	-0.7	145.6	78.6
Tonga	Tonga	Tonga	Asia-Pacific	108.0	23.0	57.7	59.2	26.6	38.1	85.5	40.9	93.4	75.3	69.9	69.4	73.6	40.0	20.0	5.7	20.0	25.0	20.7	44.4	0.1	$0.6	3.1	2.4	$5,608	1.1	8.0	13.8	48.0
Trinidad and Tobago	Trinidad and Tobago	TrinidadTobago	Americas	112.0	22.0	57.0	52.3	40.6	32.9	82.3	61.9	16.6	67.8	75.6	75.1	68.4	60.0	50.0	8.3	25.0	25.0	22.8	35.7	1.4	$43.0	-2.6	-1.3	$31,367	4.8	1.9	178.7	41.3
Tunisia	Tunisia	Tunisia	Middle East and North Africa	125.0	10.0	55.4	49.2	42.7	36.6	74.4	74.4	37.9	76.7	50.3	76.0	71.4	45.0	30.0	9.3	35.0	30.0	20.8	29.2	11.5	$135.4	1.9	1.7	$11,755	15.4	5.3	879.5	71.3
Turkey	Turkey	Turkey	Europe	68.0	33.0	64.6	55.8	49.8	41.2	76.4	65.1	92.2	66.0	49.2	70.0	79.6	70.0	60.0	2.7	35.0	22.0	25.5	34.1	80.8	$2,173.2	7.0	6.0	$26,893	11.3	11.1	10,864.0	28.5
Turkmenistan	Turkmenistan	Turkmenistan	Asia-Pacific	164.0	40.0	48.4	31.6	29.8	20.3	95.9	92.0	92.3	30.0	20.0	73.4	76.0	10.0	10.0	2.0	10.0	8.0	15.6	16.4	5.7	$103.5	6.5	7.9	$18,126	3.4	8.0	2,313.5	28.8
Uganda	Uganda	Uganda	Sub-Saharan Africa	95.0	8.0	59.7	42.2	38.5	25.4	73.3	88.7	68.6	46.3	83.2	80.1	75.4	55.0	40.0	7.3	40.0	30.0	12.9	19.4	37.7	$88.7	4.5	4.3	$2,354	2.1	5.6	699.7	39.0
Ukraine	Ukraine	Ukraine	Europe	147.0	44.0	52.3	43.9	31.5	29.6	81.8	46.9	82.6	66.1	46.7	58.6	75.0	35.0	30.0	2.5	20.0	18.0	33.1	42.1	42.3	$368.8	2.5	-2.3	$8,713	9.5	14.4	2,202.0	75.6
United Arab Emirates	United Arab Emirates	UnitedArabEmirates	Middle East and North Africa	9.0	1.0	77.6	81.8	87.1	78.8	99.2	68.8	88.9	79.9	81.1	80.9	84.4	40.0	60.0	2.8	0.0	0.0	8.9	32.3	10.1	$686.8	0.5	3.3	$67,741	1.7	2.0	10,354.2	19.5
United Kingdom	United Kingdom	UnitedKingdom	Europe	7.0	3.0	78.9	92.3	85.9	83.8	64.7	48.2	68.6	92.9	73.5	81.2	86.0	90.0	80.0	2.0	45.0	20.0	33.2	41.6	66.1	$2,914.0	1.8	2.2	$44,118	4.3	2.7	15,090.0	87.0
United States	United States	UnitedStates	Americas	12.0	2.0	76.8	79.3	78.6	77.4	75.1	57.1	53.1	83.8	89.4	76.6	86.6	85.0	80.0	1.7	37.0	21.0	26.0	37.8	325.9	$19,390.6	2.3	2.2	$59,501	4.4	2.1	275,381.0	107.8
Uruguay	Uruguay	Uruguay	Americas	40.0	6.0	68.6	68.3	58.9	69.2	77.2	67.5	69.9	74.3	71.9	72.9	78.6	85.0	30.0	5.7	30.0	25.0	27.4	32.9	3.5	$78.1	3.1	2.6	$22,371	7.9	6.2	-124.6	66.2
Uzbekistan	Uzbekistan	Uzbekistan	Asia-Pacific	140.0	36.0	53.3	49.8	34.3	25.2	91.3	67.4	98.7	72.5	58.7	58.9	62.6	10.0	10.0	8.7	22.0	7.5	18.2	33.0	32.1	$222.6	5.3	7.4	$6,929	7.2	12.5	95.8	24.5
Vanuatu	Vanuatu	Vanuatu	Asia-Pacific	116.0	26.0	56.4	65.9	36.4	51.9	97.3	54.1	15.3	52.4	58.8	75.0	64.4	65.0	40.0	7.8	0.0	0.0	16.3	39.1	0.3	$0.8	4.2	2.4	$2,739	5.2	3.1	24.7	48.4
Venezuela	Venezuela	Venezuela	Americas	179.0	32.0	25.9	7.6	13.1	7.9	74.7	58.1	17.6	33.9	28.0	0.0	60.0	0.0	10.0	10.0	34.0	34.0	14.9	37.4	31.4	$380.7	-14.0	-7.8	$12,114	7.7	1087.5	-68.0	34.9
Vietnam	Vietnam	Vietnam	Asia-Pacific	128.0	30.0	55.3	49.8	40.3	34.0	79.7	74.1	40.7	63.5	62.8	68.9	79.2	30.0	40.0	2.9	35.0	22.0	18.0	29.4	93.6	$647.4	6.8	6.2	$6,913	2.1	3.5	14,100.0	58.2
Zambia	Zambia	Zambia	Sub-Saharan Africa	138.0	27.0	53.6	45.0	35.6	32.3	72.3	80.1	12.3	71.1	46.0	70.3	72.6	55.0	50.0	6.2	35.0	35.0	17.9	25.8	17.2	$68.9	3.6	4.0	$3,996	7.8	6.6	1,091.2	62.2
Zimbabwe	Zimbabwe	Zimbabwe	Sub-Saharan Africa	175.0	45.0	40.4	29.7	24.8	15.8	62.3	74.5	23.7	33.4	43.3	72.4	70.0	25.0	10.0	5.0	51.5	25.0	22.3	29.2	14.9	$34.0	3.0	2.6	$2,283	5.0	1.3	289.4	78.4

	Country_Name	WEBNAME	Region	World_Rank	Region_Rank	2019_Score	Property_Rights	Judical_Effectiveness	Government_Integrity	Tax_Burden	Government_Spending	Fiscal_Health	Business_Freedom	Labor_Freedom	Monetary_Freedom	Trade_Freedom	Investment_Freedom	Financial_Freedom	Tariff_Rate_Percent	Income_Tax_Rate_Percent	Corporate_Tax_Rate_Percent	Tax_Burden_Percent_of_GDP	Government_Expenditure_Percent_of_GDP	Population_in_Millions	GDP_in_Billions_by_PPP_in_USA_Dollars	GDP_Growth_Rate_Percent	5_Year_GDP_Growth_Rate_Percent	GDP_per_Capita_by_PPP_in_USA_Dollars	Unemployment_Percent	Inflation_Percent	FDI_Inflow_in_Millions	Public_Debt_by_Percent_of_GDP
Country
Afghanistan	Afghanistan	Afghanistan	Asia-Pacific	152.0	39.0	51.5	19.6	29.6	25.2	91.7	80.3	99.3	49.2	60.4	76.7	66.0	10.0	10.0	7.0	20.0	20.0	5.0	25.6	35.5	69.6	2.5	2.9	1958	8.8	5.0	53.9	7.3
Albania	Albania	Albania	Europe	52.0	27.0	66.5	54.8	30.6	40.4	86.3	73.9	80.6	69.3	52.7	81.5	87.8	70.0	70.0	1.1	23.0	15.0	24.9	29.5	2.9	36.0	3.9	2.5	12507	13.9	2.0	1,119.1	71.2
Algeria	Algeria	Algeria	Middle East and North Africa	171.0	14.0	46.2	31.6	36.2	28.9	76.4	48.7	18.7	61.6	49.9	74.9	67.4	30.0	30.0	8.8	35.0	23.0	24.5	41.4	41.5	632.9	2.0	3.1	15237	10.0	5.6	1,203.0	25.8
Angola	Angola	Angola	Sub-Saharan Africa	156.0	33.0	50.6	35.9	26.6	20.5	83.9	80.7	58.2	55.7	58.8	55.4	61.2	30.0	40.0	9.4	17.0	30.0	20.6	25.3	28.2	190.3	0.7	2.9	6753	8.2	31.7	-2,254.5	65.3
Argentina	Argentina	Argentina	Americas	148.0	26.0	52.2	47.8	44.5	33.5	69.3	49.5	33.0	56.4	46.9	60.2	70.0	55.0	60.0	7.5	35.0	30.0	30.8	41.0	44.1	920.2	2.9	0.7	20876	8.7	25.7	11,857.0	52.6

Unsupervised Learning - K-Means Clustering and Hierarchical Clustering - The Heritage Foundation's Economic Freedom Index Analysis 2019 - By David Salako.¶

Background and Context¶

Objective:¶

Data Dictionary & Description:¶

Data Source¶

Importing the necessary libraries.¶

Data Preprocessing.¶

Processing columns.¶

Exploratory Data Analysis (EDA).¶

Univariate Analysis.¶

Bivariate Analysis¶

Data Preprocessing¶

Scaling¶

Clustering¶

K-means Clustering¶

Hierarchical Clustering¶

Clustering Profiling and Comparison¶

K-means Clustering¶

Insights¶

Hierarchical Clustering¶

Insights¶

K-Means Clustering vs Hierarchical Clustering Comparison¶

Insights¶

	2019_Score	Property_Rights	Judical_Effectiveness	Government_Integrity	Tax_Burden	Government_Spending	Fiscal_Health	Business_Freedom	Labor_Freedom	Monetary_Freedom	Trade_Freedom	Investment_Freedom	Financial_Freedom	Income_Tax_Rate_Percent	Corporate_Tax_Rate_Percent	Tax_Burden_Percent_of_GDP	Government_Expenditure_Percent_of_GDP	Population_in_Millions	GDP_in_Billions_by_PPP_in_USA_Dollars	GDP_Growth_Rate_Percent	5_Year_GDP_Growth_Rate_Percent	GDP_per_Capita_by_PPP_in_USA_Dollars	Unemployment_Percent	Inflation_Percent	FDI_Inflow_in_Millions	Public_Debt_by_Percent_of_GDP
Country
Afghanistan	-0.959866	-1.771283	-0.934381	-0.859921	1.190865	0.683410	1.057424	-1.053944	0.025371	0.107381	-0.835841	-2.276011	-2.101765	-0.651980	-0.442440	-1.683992	-0.620165	-0.044102	-0.265930	-0.288490	-0.110439	-0.852011	0.259212	-0.071310	-0.312060	-1.462449
Albania	0.501988	0.064576	-0.877563	-0.094849	0.731399	0.377140	0.448782	0.310799	-0.535102	0.614025	1.183693	0.534815	1.078456	-0.424652	-1.007244	0.281988	-0.243612	-0.262209	-0.279483	0.222272	-0.273044	-0.386777	1.159969	-0.107754	-0.271768	0.430524
Algeria	-1.476388	-1.145422	-0.559381	-0.673687	-0.110957	-0.828800	-1.565920	-0.212013	-0.738910	-0.082610	-0.706146	-1.339069	-1.041691	0.484660	-0.103558	0.242471	0.905356	-0.003960	-0.038708	-0.470905	-0.029137	-0.266378	0.471155	-0.064021	-0.268594	-0.914405
Angola	-1.047577	-0.921155	-1.104836	-1.096490	0.527191	0.702552	-0.280286	-0.612610	-0.091091	-2.140851	-1.280509	-1.339069	-0.511654	-0.879308	0.687167	-0.142822	-0.649130	-0.092942	-0.217242	-0.945184	-0.110439	-0.640541	0.153240	0.253049	-0.399377	0.255742
Argentina	-0.891646	-0.300510	-0.087789	-0.442151	-0.715070	-0.790516	-1.100488	-0.565081	-0.957276	-1.634207	-0.465284	-0.167891	0.548420	0.484660	0.687167	0.864867	0.866735	0.013435	0.077183	-0.142558	-1.004764	-0.017685	0.241550	0.180159	0.134403	-0.120482

	2019_Score	Property_Rights	Judical_Effectiveness	Government_Integrity	Tax_Burden	Government_Spending	Fiscal_Health	Business_Freedom	Labor_Freedom	Monetary_Freedom	Trade_Freedom	Investment_Freedom	Financial_Freedom	Income_Tax_Rate_Percent	Corporate_Tax_Rate_Percent	Tax_Burden_Percent_of_GDP	Government_Expenditure_Percent_of_GDP	Population_in_Millions	GDP_in_Billions_by_PPP_in_USA_Dollars	GDP_Growth_Rate_Percent	5_Year_GDP_Growth_Rate_Percent	GDP_per_Capita_by_PPP_in_USA_Dollars	Unemployment_Percent	Inflation_Percent	FDI_Inflow_in_Millions	Public_Debt_by_Percent_of_GDP	count_in_each_segment
K_means_segments
0	64.763235	58.663235	48.236765	42.820588	83.085294	63.513235	70.758824	69.804412	64.785294	77.911765	78.819118	63.970588	54.705882	20.925000	19.595588	23.154412	33.914706	20.680882	418.480882	2.994118	2.691176	24261.955882	9.161765	3.167647	4262.980882	53.569118	68
1	74.775862	83.637931	71.062069	76.000000	64.175862	46.434483	85.579310	82.403448	64.951724	82.758621	86.310345	81.896552	72.068966	41.565517	22.731034	33.486207	40.975862	24.220690	1098.879310	3.086207	2.593103	50956.413793	5.865517	1.510345	20269.748276	67.958621	29
2	52.747945	36.508219	33.006849	27.741096	78.178082	76.045205	56.454795	52.924658	53.486301	70.879452	66.906849	44.657534	36.164384	30.236986	28.321918	16.679452	26.693151	29.319178	188.489041	3.565753	3.753425	6438.739726	6.339726	22.109589	1468.976712	54.302740	73
3	63.466667	62.166667	71.800000	58.100000	74.966667	68.166667	47.933333	65.700000	65.133333	73.633333	77.333333	50.000000	46.666667	37.633333	26.133333	16.900000	32.300000	1010.966667	17336.233333	5.300000	5.500000	27781.333333	4.200000	2.433333	150539.033333	75.266667	3

	2019_Score	Property_Rights	Judical_Effectiveness	Government_Integrity	Tax_Burden	Government_Spending	Fiscal_Health	Business_Freedom	Labor_Freedom	Monetary_Freedom	Trade_Freedom	Investment_Freedom	Financial_Freedom	Income_Tax_Rate_Percent	Corporate_Tax_Rate_Percent	Tax_Burden_Percent_of_GDP	Government_Expenditure_Percent_of_GDP	Population_in_Millions	GDP_in_Billions_by_PPP_in_USA_Dollars	GDP_Growth_Rate_Percent	5_Year_GDP_Growth_Rate_Percent	GDP_per_Capita_by_PPP_in_USA_Dollars	Unemployment_Percent	Inflation_Percent	FDI_Inflow_in_Millions	Public_Debt_by_Percent_of_GDP	count_in_each_segment
HC_segments
0	52.939474	37.067105	33.168421	27.685526	78.410526	75.585526	56.781579	53.288158	52.955263	71.000000	67.207895	45.263158	36.842105	29.773684	27.940789	17.030263	26.981579	31.032895	224.847368	3.547368	3.735526	6593.657895	6.384211	21.415789	2243.651316	54.097368	76
1	73.828125	81.746875	68.975000	73.000000	64.831250	46.356250	85.031250	81.590625	63.853125	82.746875	86.212500	80.625000	70.937500	41.325000	22.146875	33.375000	40.987500	23.003125	1022.840625	2.890625	2.250000	48422.250000	6.775000	1.487500	18870.643750	71.918750	32
2	65.114516	58.817742	48.748387	43.561290	83.614516	64.337097	70.616129	69.985484	66.541935	77.875806	78.714516	63.951613	54.516129	20.167742	19.790323	22.595161	33.562903	18.619355	391.364516	3.085484	2.843548	24950.758065	8.933871	3.193548	3396.170968	51.045161	62
3	63.466667	62.166667	71.800000	58.100000	74.966667	68.166667	47.933333	65.700000	65.133333	73.633333	77.333333	50.000000	46.666667	37.633333	26.133333	16.900000	32.300000	1010.966667	17336.233333	5.300000	5.500000	27781.333333	4.200000	2.433333	150539.033333	75.266667	3

GDP_in_Billions_by_PPP_in_USA_Dollars	0.6	0.7	0.8	1.1	1.3	2.5	3.1	3.2	3.4	3.6	3.7	5.2	6.1	6.3	6.8	6.9	7.0	8.0	8.5	8.7	9.4	11.0	11.3	11.5	11.6	12.9	17.3	17.6	19.3	19.9	20.1	21.8	22.4	23.0	24.6	25.3	26.1	26.5	27.5	28.3	28.4	28.6	28.9	30.3	30.4	31.0	31.6	33.5	34.0	35.8	36.0	36.4	36.7	38.9	39.7	41.0	41.6	43.0	43.2	44.6	46.2	49.2	53.9	57.0	62.7	64.3	68.3	68.5	68.9	69.6	70.4	71.1	71.8	78.1	78.6	83.6	83.9	87.7	88.7	88.9	89.1	91.2	96.9	101.3	103.5	103.9	105.5	133.7	135.4	137.8	153.1	162.2	163.1	171.8	172.4	178.9	179.4	186.6	187.0	188.6	190.3	192.6	200.2	222.6	244.0	274.7	286.8	289.0	291.5	298.6	298.7	313.4	316.5	328.7	340.6	357.2	368.8	375.7	380.0	380.7	424.4	439.6	451.1	454.9	477.6	481.5	517.2	520.9	527.0	528.5	632.9	647.4	686.8	687.1	714.0	765.6	875.6	916.1	920.2	930.8	1057.0	1118.8	1121.0	1185.5	1201.2	1233.7	1246.5	1644.7	1769.3	1773.6	1773.9	2029.0	2173.2	2310.9	2458.4	2835.8	2914.0	3240.3	3242.8	4007.8	4170.8	5428.8	9459.0	19390.6	23159.1
HC_segments
0	0	1	0	0	2	0	1	1	1	2	0	0	1	1	1	0	0	1	1	0	1	0	0	1	0	1	1	0	0	1	1	1	1	0	0	1	0	1	0	0	1	1	1	1	1	0	0	0	1	1	0	1	2	0	1	1	0	0	1	0	1	1	0	1	0	1	1	1	1	1	0	0	0	0	1	1	0	1	1	1	0	0	1	0	1	0	0	1	1	1	0	1	1	0	1	0	0	0	1	0	1	1	1	1	0	1	0	0	0	0	0	0	0	1	0	0	1	0	0	1	0	0	0	0	0	0	0	0	0	0	1	1	0	1	0	0	1	0	1	0	1	1	0	0	1	0	0	1	0	0	0	0	0	0	0	0	0	1	0	0	0	0	0	0	0
1	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	1	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	1	0	0	0	0	0	0	0	0	0	1	0	0	0	0	0	0	0	1	0	0	0	0	0	0	1	0	0	0	0	0	0	0	0	0	0	0	1	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	1	0	0	0	0	1	0	1	0	0	0	1	1	1	0	0	1	0	0	1	0	0	1	1	1	0	0	1	1	1	1	0	0	0	0	0	0	0	1	0	0	0	0	0	1	0	0	1	0	1	0	1	1	0	1	0	1	1	0	0	0	1	1	0	0	0
2	1	0	1	1	1	1	0	0	0	0	1	1	0	0	0	1	2	0	0	1	0	1	1	0	1	0	0	0	1	0	0	0	0	1	1	0	1	1	1	1	0	0	0	0	0	1	0	1	0	0	1	0	0	1	2	0	0	1	0	1	0	0	1	0	0	0	0	0	0	0	1	0	1	1	0	0	1	0	0	0	1	1	0	0	0	1	1	0	0	0	1	0	0	1	0	1	1	1	0	0	0	0	0	0	0	0	0	1	1	1	0	0	0	0	1	0	0	1	0	0	1	0	0	0	1	1	0	0	0	0	0	0	1	0	1	1	0	0	0	1	0	0	1	0	0	1	0	0	0	1	0	0	1	0	1	0	0	0	1	1	0	0	0	0	0
3	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	1	1	1

FDI_Inflow_in_Millions	-8296.9	-3114.7	-2254.5	-200.5	-136.8	-124.6	-87.3	-68.0	-46.3	-5.4	0.3	6.7	8.6	9.0	10.3	13.8	16.6	17.2	24.7	36.5	41.0	53.9	55.5	77.0	87.1	87.5	92.4	93.8	95.8	108.6	135.0	141.3	145.6	165.0	178.7	184.4	198.0	212.2	213.8	245.7	247.8	256.3	265.6	277.1	286.2	289.4	292.7	299.0	300.5	304.1	329.6	334.3	335.0	355.8	366.2	374.9	389.1	400.6	416.0	425.2	485.9	517.5	518.9	532.3	545.9	560.0	576.5	595.4	606.4	671.7	672.5	674.7	699.7	702.0	721.2	724.7	740.4	784.4	791.9	813.0	879.5	888.0	896.6	927.7	986.0	1065.3	1070.7	1091.2	1119.1	1146.7	1158.8	1180.4	1185.7	1203.0	1276.4	1324.7	1327.9	1340.2	1374.9	1421.0	1494.4	1498.0	1664.8	1861.9	1867.4	1996.8	2104.2	2151.6	2202.0	2276.7	2293.1	2313.5	2491.6	2628.0	2651.4	2784.4	2806.0	2866.7	2867.0	3007.1	3185.2	3255.0	3503.0	3570.0	3572.0	3586.4	4046.0	4341.0	4633.7	5019.0	5160.0	5319.2	6343.3	6433.5	6622.7	6729.6	6769.5	6945.6	7391.7	7412.2	7635.2	9524.3	9543.4	9629.6	10354.2	10429.8	10864.0	11857.0	14100.0	14518.0	15090.0	15395.7	17052.8	17077.1	18954.0	19086.1	23063.1	24243.8	25284.0	28974.6	29695.0	34726.3	39916.1	40986.1	46368.0	49794.9	57956.7	62006.0	62712.6	104333.0	136320.0	275381.0
HC_segments
0	0	0	1	1	0	0	1	1	0	0	1	1	1	0	0	0	1	1	0	1	1	1	1	1	0	1	0	0	1	0	0	1	1	1	0	1	1	1	1	0	1	0	1	1	0	1	0	0	0	1	1	1	1	1	0	1	1	0	0	0	1	0	0	1	0	1	1	0	1	1	1	1	1	0	0	1	0	0	1	1	1	0	1	0	0	1	0	1	0	1	1	1	1	1	0	0	0	1	1	0	0	1	0	0	0	0	0	1	1	0	1	1	0	1	0	1	1	0	0	0	0	1	1	1	0	1	0	1	0	1	0	0	0	0	0	0	0	0	1	0	0	1	0	0	0	0	0	1	1	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	1	0	0	0
1	1	1	0	0	0	0	0	0	0	1	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	1	0	0	1	1	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	1	0	0	0	0	0	0	0	0	0	1	0	0	0	0	0	0	0	0	0	0	0	0	0	0	1	0	0	1	0	1	0	0	0	0	0	1	0	1	1	0	1	0	0	0	0	0	1	0	1	0	0	0	0	1	1	1	1	1	1	0	1	0	1	0	1	0	1	1	1	1	1	0	1	0	0
2	0	0	0	0	1	1	0	0	1	0	0	0	0	1	1	1	0	0	1	0	0	0	0	0	1	0	1	1	0	1	1	0	0	0	1	0	0	0	0	1	0	1	0	0	1	0	1	1	1	0	0	0	0	0	1	0	0	1	1	1	0	1	1	0	1	0	0	1	0	0	0	0	0	0	1	0	0	0	0	0	0	1	0	1	1	0	1	0	1	0	0	0	0	0	1	1	0	0	0	1	1	0	1	1	1	1	0	0	0	1	0	0	1	0	1	0	0	1	1	1	1	0	0	0	0	0	0	0	1	0	1	1	0	1	0	0	1	0	0	1	1	0	1	0	1	0	1	0	0	1	0	0	0	0	0	0	1	0	1	0	1	0	0	0	0	0	0	0	0	0	0	0
3	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	1	0	0	0	0	0	0	0	1	1